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Academic literature on the topic 'Task oriented dialogue system'

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Published: 25 May 2024

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Journal articles on the topic "Task oriented dialogue system"

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Xiang, Lu, Junnan Zhu, Yang Zhao, Yu Zhou, and Chengqing Zong. "Robust Cross-lingual Task-oriented Dialogue." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 6 (November 30, 2021): 1–24. http://dx.doi.org/10.1145/3457571.

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Cross-lingual dialogue systems are increasingly important in e-commerce and customer service due to the rapid progress of globalization. In real-world system deployment, machine translation (MT) services are often used before and after the dialogue system to bridge different languages. However, noises and errors introduced in the MT process will result in the dialogue system's low robustness, making the system's performance far from satisfactory. In this article, we propose a novel MT-oriented noise enhanced framework that exploits multi-granularity MT noises and injects such noises into the dialogue system to improve the dialogue system's robustness. Specifically, we first design a method to automatically construct multi-granularity MT-oriented noises and multi-granularity adversarial examples, which contain abundant noise knowledge oriented to MT. Then, we propose two strategies to incorporate the noise knowledge: (i) Utterance-level adversarial learning and (ii) Knowledge-level guided method. The former adopts adversarial learning to learn a perturbation-invariant encoder, guiding the dialogue system to learn noise-independent hidden representations. The latter explicitly incorporates the multi-granularity noises, which contain the noise tokens and their possible correct forms, into the training and inference process, thus improving the dialogue system's robustness. Experimental results on three dialogue models, two dialogue datasets, and two language pairs have shown that the proposed framework significantly improves the performance of the cross-lingual dialogue system.
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Matsumoto, Kazuyuki, Manabu Sasayama, and Taiga Kirihara. "Topic Break Detection in Interview Dialogues Using Sentence Embedding of Utterance and Speech Intention Based on Multitask Neural Networks." Sensors 22, no. 2 (January 17, 2022): 694. http://dx.doi.org/10.3390/s22020694.

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Currently, task-oriented dialogue systems that perform specific tasks based on dialogue are widely used. Moreover, research and development of non-task-oriented dialogue systems are also actively conducted. One of the problems with these systems is that it is difficult to switch topics naturally. In this study, we focus on interview dialogue systems. In an interview dialogue, the dialogue system can take the initiative as an interviewer. The main task of an interview dialogue system is to obtain information about the interviewee via dialogue and to assist this individual in understanding his or her personality and strengths. In order to accomplish this task, the system needs to be flexible and appropriate for detecting topic switching and topic breaks. Given that topic switching tends to be more ambiguous in interview dialogues than in task-oriented dialogues, existing topic modeling methods that determine topic breaks based only on relationships and similarities between words are likely to fail. In this study, we propose a method for detecting topic breaks in dialogue to achieve flexible topic switching in interview dialogue systems. The proposed method is based on multi-task learning neural network that uses embedded representations of sentences to understand the context of the text and utilizes the intention of an utterance as a feature. In multi-task learning, not only topic breaks but also the intention associated with the utterance and the speaker are targets of prediction. The results of our evaluation experiments show that using utterance intentions as features improves the accuracy of topic separation estimation compared to the baseline model.
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Zhu, Qi, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, and Minlie Huang. "CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset." Transactions of the Association for Computational Linguistics 8 (July 2020): 281–95. http://dx.doi.org/10.1162/tacl_a_00314.

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To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts on both user and system sides. About 60% of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation. We also provide a user simulator and several benchmark models for pipelined task-oriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus. The large size and rich annotation of CrossWOZ make it suitable to investigate a variety of tasks in cross-domain dialogue modeling, such as dialogue state tracking, policy learning, user simulation, etc.
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Wu, Zhong, Qiping She, and Chuan Zhou. "Intelligent Customer Service System Optimization Based on Artificial Intelligence." Journal of Organizational and End User Computing 36, no. 1 (February 7, 2024): 1–27. http://dx.doi.org/10.4018/joeuc.336923.

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To elevate the intelligence of customer service dialogue systems, this article proposes an intelligent customer service system comprising chat dialogue subsystems, task-oriented multi-turn dialogue subsystems, single-turn dialogue subsystems, and an integration model. Firstly, to enhance diversity of responses and improve user experience, particularly in casual chat scenarios, this article presents a Seq2Seq-based approach for multi-answer responses, allowing for more expressive emotional expression in responses. Secondly, to address situations where customers cannot articulate their needs in a single sentence during multi-turn dialogues, this article designs a task-oriented multi-turn dialogue module. It employs intent recognition and slot filling to maintain contextual information throughout the conversation, aiding customers in problem resolution. Lastly, to overcome the current limitation of intelligent customer service models providing relatively one-dimensional answers in specific domains.
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Zhang, Zheng, Minlie Huang, Zhongzhou Zhao, Feng Ji, Haiqing Chen, and Xiaoyan Zhu. "Memory-Augmented Dialogue Management for Task-Oriented Dialogue Systems." ACM Transactions on Information Systems 37, no. 3 (July 20, 2019): 1–30. http://dx.doi.org/10.1145/3317612.

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Iizuka, Shinya, Shota Mochizuki, Atsumoto Ohashi, Sanae Yamashita, Ao Guo, and Ryuichiro Higashinaka. "Clarifying the Dialogue-Level Performance of GPT-3.5 and GPT-4 in Task-Oriented and Non-Task-Oriented Dialogue Systems." Proceedings of the AAAI Symposium Series 2, no. 1 (January 22, 2024): 182–86. http://dx.doi.org/10.1609/aaaiss.v2i1.27668.

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Although large language models such as ChatGPT and GPT-4 have achieved superb performances in various natural language processing tasks, their dialogue performance is sometimes not very clear because the evaluation is often done on the utterance level where the quality of an utterance given context is the evaluation target. Our objective in this work is to conduct human evaluations of GPT-3.5 and GPT-4 to perform MultiWOZ and persona-based chat tasks in order to verify their dialogue-level performance in task-oriented and non-task-oriented dialogue systems. Our findings show that GPT-4 performs comparably with a carefully created rule-based system and has a significantly superior performance to other systems, including those based on GPT-3.5, in persona-based chat.
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ZENG, Ya, Li WAN, Qiuhong LUO, and Mao CHEN. "A Hierarchical Memory Model for Task-Oriented Dialogue System." IEICE Transactions on Information and Systems E105.D, no. 8 (August 1, 2022): 1481–89. http://dx.doi.org/10.1587/transinf.2022edp7001.

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MORIN, PHILIPPE, JEAN-PAUL HATON, JEAN-MARIE PIERREL, GUENTHER RUSKE, and WALTER WEIGEL. "A MULTILINGUAL APPROACH TO TASK-ORIENTED MAN-MACHINE DIALOGUE BY VOICE." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 03 (September 1988): 573–88. http://dx.doi.org/10.1142/s0218001488000339.

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In the framework of man-machine communication, oral dialogue has a particular place since human speech presents several advantages when used either alone or in multimedia interfaces. The last decade has witnessed a proliferation of research into speech recognition and understanding, but few systems have been defined with a view to managing and understanding an actual man-machine dialogue. The PARTNER system that we describe in this paper proposes a solution in the case of task oriented dialogue with the use of artificial languages. A description of the essential characteristics of dialogue systems is followed by a presentation of the architecture and the principles of the PARTNER system. Finally, we present the most recent results obtained in the oral management of electronic mail in French and German.
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Young, Tom, Frank Xing, Vlad Pandelea, Jinjie Ni, and Erik Cambria. "Fusing Task-Oriented and Open-Domain Dialogues in Conversational Agents." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11622–29. http://dx.doi.org/10.1609/aaai.v36i10.21416.

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The goal of building intelligent dialogue systems has largely been separately pursued under two paradigms: task-oriented dialogue (TOD) systems, which perform task-specific functions, and open-domain dialogue (ODD) systems, which focus on non-goal-oriented chitchat. The two dialogue modes can potentially be intertwined together seamlessly in the same conversation, as easily done by a friendly human assistant. Such ability is desirable in conversational agents, as the integration makes them more accessible and useful. Our paper addresses this problem of fusing TODs and ODDs in multi-turn dialogues. Based on the popular TOD dataset MultiWOZ, we build a new dataset FusedChat, by rewriting the existing TOD turns and adding new ODD turns. This procedure constructs conversation sessions containing exchanges from both dialogue modes. It features inter-mode contextual dependency, i.e., the dialogue turns from the two modes depend on each other. Rich dependency patterns such as co-reference and ellipsis are included. The new dataset, with 60k new human-written ODD turns and 5k re-written TOD turns, offers a benchmark to test a dialogue model's ability to perform inter-mode conversations. This is a more challenging task since the model has to determine the appropriate dialogue mode and generate the response based on the inter-mode context. However, such models would better mimic human-level conversation capabilities. We evaluate two baseline models on this task, including the classification-based two-stage models and the two-in-one fused models. We publicly release FusedChat and the baselines to propel future work on inter-mode dialogue systems.
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Dong, Xuelian, and Jiale Chen. "PluDG: enhancing task-oriented dialogue system with knowledge graph plug-in module." PeerJ Computer Science 9 (November 24, 2023): e1707. http://dx.doi.org/10.7717/peerj-cs.1707.

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Task-oriented dialogue systems continue to face significant challenges as they require not only an understanding of dialogue history but also domain-specific knowledge. However, knowledge is often dynamic, making it difficult to effectively integrate into the learning process. Existing large language model approaches primarily treat knowledge bases as textual resources, neglecting to capture the underlying relationships between facts within the knowledge base. To address this limitation, we propose a novel dialogue system called PluDG. We regard the knowledge as a knowledge graph and propose a knowledge extraction plug-in, Kg-Plug, to capture the features of the graph and generate prompt entities to assist the system’s dialogue generation. Besides, we propose Unified Memory Integration, a module that enhances the comprehension of the sentence’s internal structure and optimizes the knowledge base’s encoding location. We conduct experiments on three public datasets and compare PluDG with several state-of-the-art dialogue models. The experimental results indicate that PluDG achieves significant improvements in both accuracy and diversity, outperforming the current state-of-the-art dialogue system models and achieving state-of-the-art performance.
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Dissertations / Theses on the topic "Task oriented dialogue system"

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ISOMURA, Naoki, Fujio TORIUMI, and Kenichiro ISHII. "EVALUATION METHOD OF NON-TASK-ORIENTED DIALOGUE SYSTEM BY HMM." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2006. http://hdl.handle.net/2237/10479.

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Yang, Fan. "Directing the flow of conversation in task-oriented dialogue." Full text open access at:, 2008. http://content.ohsu.edu/u?/etd,625.

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Balaraman, Vevake. "Leveraging Domain Information for Data Driven Task-Oriented Dialogue Systems." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/339652.

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Task-oriented dialogue systems are designed to interact and assist users to achieve a certain goal. A typical approach to model such systems is a component-based approach that involves multiple components, each performing a sub-task of the dialogue system. These components are generally modelled based on a given domain with domain-specific features making them expensive to port to new domains and requiring large in-domain data. In this thesis we investigate the role of domain information in data-driven task-oriented systems. We note that domain information is widely unused in current neural models, and present several approaches that incorporate and take advantage of domain knowledge in different components of a dialogue system. More specifically, this thesis: i) presents a neural model for the entity recognition task focused on low-resource domains integrating large gazetteers as domain information; ii) presents an approach for single domain dialogue state trackers that incorporates domain information; iii) presents a multi-domain dialogue state tracker, which is able to read directly from domain schema and is able to make predictions even for unseen domains; iv) provides a survey on the recent neural approaches to dialogue state tracker by grouping neural models based on their approach; and v) proposes to incorporate domain knowledge for implementing a proactive strategy of a dialogue policy component. Both results and analysis of the experiments show the effectiveness of incorporating domain information as part of network modelling. Particularly, we address current limitations of neural models for task-oriented systems, such as requiring large training data, high latency response time, and flexibility with respect to domains.
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Linné, Christoffer, and Pontus Olausson. "Crowdsourcing av data för Hybrid Code Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281968.

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Task-oriented dialogue systems are a popular way for organisations to generate extra value both internally and for customers. Modern approaches for these dialogue systems that use neural networks to enable training directly on written dialogues are very data hungry, which complicates their implementation. Crowdsourcing is an attractive solution for generating this type of training data, but the method also comes with several difficulties. We introduce a new method for generating training data based on parallel crowdsourcing of dialogues, as well as crowdsourced quality review. We use this method to collect a small dataset that takes place within the domain bus driver-traveler. We believe that this method offers an efficient way to collect new, high-quality datasets. Hybrid Code Networks is a model for dialogue systems that combines a neural network with domain-specific knowledge, and thus requires a significantly smaller amount of training data than other similar dialogue systems to achieve comparable performance. By combining Hybrid Code Networks with our new method for generating training data, we believe that the threshold for implementing task-oriented dialogue systems on domains with insufficient training data can be lowered. We implement Hybrid Code Networks and train the implementation on the collected dataset and achieve good results.
Uppgiftsorienterade dialogsystem är ett populärt sätt för företag att generera extra värde både internt och för kunder. Moderna modeller för dessa dialogsystem som använder neurala nätverk för att möjliggöra träning direkt på skriftliga dialoger är väldigt datahungriga, vilket försvårar implementationen av dessa. Crowdsourcing är en attraktiv lösning för att generera denna typ av träningsdata, men metoden kommer även med flera svårigheter. Vi introducerar en ny metod för generering av träningsdata som bygger på parallell crowdsourcing av dialoger, samt crowdsourcad kvalitetsgranskning. Vi använder denna metod för att samla in ett litet dataset som utspelar sig inom domänen busschaufför-resenär. Vi menar att denna metod erbjuder ett effektivt sätt att samla in nya, högkvalitativa dataset. Hybrid Code Networks är en modell för dialogsystem som kombinerar ett neuralt nätverk med domänspecifik kunskap, och som på så sätt kräver en betydligt mindre mängd träningsdata än andra liknande dialogsystem för att uppnå jämförbar prestanda. Genom att kombinera Hybrid Code Networks med vår nya metod för generering av träningsdata menar vi att man kan sänka tröskeln för att implementera uppgiftsorienterade dialogsystem på domäner med otillräcklig träningsdata. Vi implementerar Hybrid Code Networks och tränar implementationen på det insamlade datasetet, och uppnår goda resultat.
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Veron, Mathilde. "Systèmes de dialogue apprenant tout au long de leur vie : de l'élaboration à l'évaluation." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG089.

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Les systèmes de dialogue orientés tâche, plus communément appelés chatbots, ont pour but de réaliser des tâches et de fournir des informations à la demande d’un utilisateur dans le cadre d’une conversation et d’un domaine précis (e.g. réservation d’un billet de train). Ces systèmes ont été largement adoptés par de nombreuses entreprises. Cependant, ils souffrent en pratique de certaines limitations : (1) ils sont dépendants des données d’entraînement nécessaires afin d’obtenir un système performant, (2) ils manquent de flexibilité et sont peu performants dès que le cas de figure rencontré en pratique s’éloigne des données vues pendant le développement, et (3) il est difficile de les adapter au cours du temps aux nouveaux éléments qui apparaissent étant donné l’évolution inévitable du monde et des exigences des concepteurs et des utilisateurs. Ainsi, nous appliquons le Lifelong Learning (LL) aux systèmes de dialogue orientés tâche. Nous définissons le LL comme la capacité d’un système à être appliqué à et à apprendre plusieurs tâches au cours du temps, en production, en autonomie, en continu et de manière interactive. Trois étapes doivent alors être réalisées en autonomie par le système : (1) Détecter la présence d’un nouvel élément, (2) extraire et identifier le nouvel élément et (3) adapter les composants du système associés à cet élément. Dans le cadre de cette thèse et étant donné la complexité du sujet, nous nous concentrons sur trois sous-problèmes liés aux systèmes de dialogue apprenant tout au long de leur vie. Dans un 1er temps, nous proposons une 1ère méthodologie pour l’évaluation continue et au cours du temps de l’apprentissage sur le terrain des systèmes de dialogue. Ce type d’apprentissage est proche du LL mais met de côté l’aspect multi-tâches. Nous décrivons aussi un système de dialogue orienté tâche capable d’améliorer sur le terrain sa détection des slots via l’annotation autonome de données collectées au cours de ses interactions. Nous évaluons ce système à travers deux méthodes d’adaptation grâce à notre méthodologie et montrons l’intérêt d’une évaluation continue et au cours du temps. Dans un 2nd temps, nous nous concentrons sur l’étude novatrice du transfert inter-langue dans le cadre de l’apprentissage continu d’une séquence de langues. En effet, le transfert et l’apprentissage continu sont deux aspects importants du LL. Nous réalisons cette étude sur la tâche de détection des slots à l’aide de BERT multilingue. Nous observons des capacités de transfert en avant substantielles malgré la présence d’oubli et présentons les capacités d’un modèle entraîné de manière continue. Dans un 3ème temps, nous nous intéressons à l’étude du transfert inter-domaine dans le cadre de l’apprentissage zero-shot. Nous réalisons cette étude sur la tâche de suivi de l’état du dialogue, qui nécessite de considérer l’ensemble du dialogue et plus seulement le tour courant. Nous étudions d’abord les capacités de généralisation et de transfert d’un modèle existant sur de nouvelles valeurs de slots. Ensuite, nous proposons des variantes du modèle et une méthode capable d’améliorer les performances zero-shot du modèle sur des nouveaux types de slots appartenant à un nouveau domaine
Task-oriented dialogue systems, more commonly known as chatbots, are intended to perform tasks and provide the information required by a user in a conversation in a specific domain (e.g., train booking). These systems have been widely adopted by many companies. However, they suffer in practice from some limitations: (1) they are dependent on the training data needed to obtain a performing system, (2) they lack flexibility and perform poorly as soon as the case encountered in practice moves away from the data seen during development, and (3) it is difficult to adapt them over time to new elements that appear given the inevitable evolution of the world, of the requirements of the designers and users. Thus, we apply Lifelong Learning (LL) to task-oriented dialogue systems. We define LL as the ability of a system to be applied to and learn multiple tasks over time, in production, autonomously, continuously, and interactively. Three steps must be performed in autonomy by the system: (1) Detect the presence of a new element, (2) extract and identify the new element, and (3) adapt the system components associated with this element. As part of this thesis and given the complexity of LL, we focus our work on three subproblems associated with LL dialogue systems. As a first step, we propose a first methodology for the continuous and time-dependent evaluation of on-the-job learning dialogue systems. This type of learning is close to LL but puts aside the multitask aspect. We also describe a task-oriented dialogue system capable of improving its slot detection on-the-job via the autonomous annotation of data collected during its interactions. We evaluate this system through two adaptation methods using our methodology and show interest in a continuous evaluation over time. As a second step, we focus on the innovative study of interlingual transfer when applying continual learning to a language sequence. Indeed, transfer and continual learning are two main aspects of LL. We perform this study on the slot-filling task using multilingual BERT. We observe substantial forward transfer capabilities despite the presence of forgetting and demonstrate the capabilities of a model trained in a continual manner. As a third step, we study inter-domain transfer in the context of zero-shot learning. We carry out this study on a task that requires considering the whole dialogue and not only the current turn, which corresponds to the dialogue state tracking task. We first study the generalization and transfer capabilities of an existing model on new slot values. Then, we propose some model variants and a method able to improve the zero-shot performance of the model on new types of slots belonging to a new domain
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Bouguelia, Sara. "Modèles de dialogue et reconnaissance d'intentions composites dans les conversations Utilisateur-Chatbot orientées tâches." Electronic Thesis or Diss., Lyon 1, 2023. http://www.theses.fr/2023LYO10106.

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Les Systèmes de Dialogue (ou simplement chatbots) sont très demandés de nos jours. Ils permettent de comprendre les besoins des utilisateurs (ou intentions des utilisateurs), exprimés en langage naturel, et de répondre à ces intentions en invoquant les APIs (Interfaces de Programmation d’Application) appropriées. Les chatbots sont connus pour leur interface facile à utiliser et ils ne nécessitent que l'une des capacités les plus innées des humains qui est l'utilisation du langage naturel. L'amélioration continue de l'Intelligence Artificielle (IA), du Traitement du Langage Naturel (NLP) et du nombre incalculable de dispositifs permettent d'effectuer des tâches réelles (par exemple, faire une réservation) en utilisant des interactions basées sur le langage naturel entre les utilisateurs et un grand nombre de services.Néanmoins, le développement de chatbots est encore à un stade préliminaire, avec plusieurs défis théoriques et techniques non résolus découlant de (i) la variations d'énoncés dans les interactions humain-chatbot en libre échange et (ii) du grand nombre de services logiciels potentiellement inconnus au moment du développement. Les conversations en langage naturel des personnes peuvent être riches, potentiellement ambiguës et exprimer des intentions complexes et dépendantes du contexte. Les techniques traditionnelles de modélisation et d'orchestration de processus et de composition de services sont limitées pour soutenir de telles conversations car elles supposent généralement une attente a priori de quelles informations et applications seront accédées et comment les utilisateurs exploreront ces sources et services. Limiter les conversations à un modèle de processus signifie que nous ne pouvons soutenir qu'une petite fraction de conversations possibles. Bien que les avancées existantes dans les techniques de NLP et d'apprentissage automatique (ML) automatisent diverses tâches telles que la reconnaissance d'intention, la synthèse d'appels API pour prendre en charge une large gamme d'intentions d'utilisateurs potentiellement complexes est encore largement un processus manuel et coûteux.Ce projet de thèse vise à faire avancer la compréhension fondamentale de l'ingénierie des services cognitifs. Dans cette thèse, nous contribuons à des abstractions et des techniques novatrices axées sur la synthèse d'appels API pour soutenir une large gamme d'intentions d'utilisateurs potentiellement complexes. Nous proposons des techniques réutilisables et extensibles pour reconnaître et réaliser des intentions complexes lors des interactions entre humains, chatbots et services. Ces abstractions et techniques visent à débloquer l'intégration transparente et évolutive de conversations basées sur le langage naturel avec des services activés par logiciel
Dialogue Systems (or simply chatbots) are in very high demand these days. They enable the understanding of user needs (or user intents), expressed in natural language, and on fulfilling such intents by invoking the appropriate back-end APIs (Application Programming Interfaces). Chatbots are famed for their easy-to-use interface and gentle learning curve (it only requires one of humans' most innate ability, the use of natural language). The continuous improvement in Artificial Intelligence (AI), Natural Language Processing (NLP), and the countless number of devices allow performing real-world tasks (e.g., making a reservation) by using natural language-based interactions between users and a large number of software enabled services.Nonetheless, chatbot development is still in its preliminary stage, and there are several theoretical and technical challenges that need to be addressed. One of the challenges stems from the wide range of utterance variations in open-end human-chatbot interactions. Additionally, there is a vast space of software services that may be unknown at development time. Natural human conversations can be rich, potentially ambiguous, and express complex and context-dependent intents. Traditional business process and service composition modeling and orchestration techniques are limited to support such conversations because they usually assume a priori expectation of what information and applications will be accessed and how users will explore these sources and services. Limiting conversations to a process model means that we can only support a small fraction of possible conversations. While existing advances in NLP and Machine Learning (ML) techniques automate various tasks such as intent recognition, the synthesis of API calls to support a broad range of potentially complex user intents is still largely a manual, ad-hoc and costly process.This thesis project aims at advancing the fundamental understanding of cognitive services engineering. In this thesis we contribute novel abstractions and techniques focusing on the synthesis of API calls to support a broad range of potentially complex user intents. We propose reusable and extensible techniques to recognize and realize complex intents during humans-chatbots-services interactions. These abstractions and techniques seek to unlock the seamless and scalable integration of natural language-based conversations with software-enabled services
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Schaub, Léon-Paul. "Dimensions mémorielles de l'interaction écrite humain-machine ˸ une approche cognitive par les modèles mnémoniques pour la détection et la correction des incohérences du système dans les dialogues orientés-tâche." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG023.

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Dans ce travail, nous nous intéressons à la place des systèmes de dialogue orientés-tâche à la fois dans le traitement automatique des langues, et dans l’interaction humain-machine. Nous nous concentrons plus particulièrement sur la différence de traitement de l’information et de l’utilisation de la mémoire, d’un tour de parole à l’autre, par l’humain et la machine, pendant une conversation écrite de type clavardage. Après avoir étudié les mécanismes de rétention et de rappel mémoriels chez l’humain durant un dialogue, en particulier dans l'accomplissement d'une tâche, nous émettons l’hypothèse qu’un des éléments susceptible d'expliquer que les performances des machines demeurent en deçà de celles des humains, est la capacité à posséder non seulement une image de l’utilisateur, mais également une image de soi, explicitement convoquée pendant les inférences liées à la poursuite du dialogue. Cela se traduit pour le système par les trois axes suivants. Tout d’abord, par l’anticipation, à un tour de parole donné, du tour suivant de l’utilisateur. Ensuite, par la détection d’une incohérence dans son propre énoncé, facilitée, comme nous le démontrons, par l’anticipation du tour suivant de l’utilisateur en tant qu’indice supplémentaire. Enfin, par la prévision du nombre de tours de paroles restants dans le dialogue afin d’avoir une meilleure vision de la progression du dialogue, en prenant en compte la potentielle présence d’une incohérence dans son propre énoncé, c’est que nous appelons le double modèle du système, qui représente à la fois l’utilisateur et l’image que le système renvoie à l’utilisateur. Pour mettre en place ces fonctionnalités, nous exploitons les réseaux de mémoire de bout-en-bout, un modèle de réseau de neurones récurrent qui possède la spécificité non seulement de traiter des historiques de dialogue longs (comme un RNN ou un LSTM) mais également de créer des sauts de réflexion, permettant de filtrer l’information contenue à la fois dans l’énoncé de l’utilisateur et dans celui de l’historique de dialogue. De plus, ces trois sauts de réflexion servent de mécanisme d’attention “naturel” pour le réseau de mémoire, à la manière d’un décodeur de transformeur. Pour notre étude, nous améliorons, en y ajoutant nos trois fonctionnalités, un type de réseau de mémoire appelé WMM2Seq (réseau de mémoire de travail par séquence). Ce modèle s’inspire des modèles cognitifs de la mémoire, en présentant les concepts de mémoire épisodique, de mémoire sémantique et de mémoire de travail. Il obtient des résultats performants sur des tâches de génération de réponse de dialogue sur les corpus DSTC2 (humain-machine dans le domaine de restaurant) et MultiWOZ (multi-domaine créé avec Magicien d’Oz); ce sont les corpus que nous utilisons pour nos expériences. Les trois axes mentionnés précédemment apportent deux contributions principales à l’existant. En premier lieu, ceci complexifie l’intelligence du système de dialogue en le dotant d’un garde-fou (incohérences détectées). En second lieu, cela optimise à la fois le traitement des informations dans le dialogue (réponses plus précises ou plus riches) et la durée de celui-ci. Nous évaluons les performances de notre système avec premièrement la f-mesure pour les entités détectées à chaque tour de parole, deuxièmement de score BLEU pour la fluidité de l’énoncé du système et troisièmement de taux d’exactitude jointe pour la réussite du dialogue. Les résultats obtenus montrent l’intérêt d’orienter les recherches vers des modèles de gestion de la mémoire plus cognitifs afin de réduire l’écart de performance dans un dialogue entre l’humain et la machine
In this work, we are interested in the place of task-oriented dialogue systems in both automatic language processing and human-machine interaction. In particular, we focus on the difference in information processing and memory use, from one turn to the next, by humans and machines, during a written chat conversation. After having studied the mechanisms of memory retention and recall in humans during a dialogue, in particular during the accomplishment of a task, we hypothesize that one of the elements that may explain why the performance of machines remains below that of humans, is the ability to possess not only an image of the user, but also an image of oneself, explicitly summoned during the inferences linked to the continuation of the dialogue. This translates into the following three axes for the system. First, by the anticipation, at a given turn of speech, of the next turn of the user. Secondly, by the detection of an inconsistency in one's own utterance, facilitated, as we demonstrate, by the anticipation of the user's next turn as an additional cue. Finally, by predicting the number of remaining turns in the dialogue in order to have a better vision of the dialogue progression, taking into account the potential presence of an incoherence in one's own utterance, this is what we call the dual model of the system, which represents both the user and the image that the system sends to the user. To implement these features, we exploit end-to-end memory networks, a recurrent neural network model that has the specificity not only to handle long dialogue histories (such as an RNN or an LSTM) but also to create reflection jumps, allowing to filter the information contained in both the user's utterance and the dialogue history. In addition, these three reflection jumps serve as a "natural" attention mechanism for the memory network, similar to a transformer decoder. For our study, we enhance a type of memory network called WMM2Seq (sequence-based working memory network) by adding our three features. This model is inspired by cognitive models of memory, presenting the concepts of episodic memory, semantic memory and working memory. It performs well on dialogue response generation tasks on the DSTC2 (human-machine in the restaurant domain) and MultiWOZ (multi-domain created with Wizard of Oz) corpora; these are the corpora we use for our experiments. The three axes mentioned above bring two main contributions to the existing. Firstly, it adds complexity to the intelligence of the dialogue system by providing it with a safeguard (detected inconsistencies). Second, it optimizes both the processing of information in the dialogue (more accurate or richer answers) and the duration of the dialogue. We evaluate the performance of our system with firstly the F1 score for the entities detected in each speech turn, secondly the BLEU score for the fluency of the system utterance and thirdly the joint accuracy for the success of the dialogue. The results obtained show that it would be interesting to direct research towards more cognitive models of memory management in order to reduce the performance gap in a human-machine dialogue
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Kowtko, J. C. "The function of intonation in task-oriented dialogue." Thesis, University of Edinburgh, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.508706.

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Carletta, Jean. "Risk-taking and recovery in task-oriented dialogue." Thesis, University of Edinburgh, 1992. http://hdl.handle.net/1842/20370.

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The Principle of Parsimony states that by and large, agents try to complete tasks using as little effort as possible. This thesis demonstrates that the Principle of Parsimony operates in human task-oriented dialogue by showing the effects of Parsimony in a corpus of human dialogues about a map navigation task and by using the main points of the analysis in order to guide simulated conversations between two computer agents within the JAM system. It makes four major contributions: an analysis of 'communicative posture', or a range of choices in dialogue which can be characterised by decisions about how much effort to spend constructing one's utterances, leading to either careful or risky behaviour about different aspects of communication, an analysis of 'recovery strategies' which allow the participants to recover from failures which have been brought about due to risky postures, a heuristic model of belief which risks failing to capture the full meaning of the dialogue in favour of efficiency in a way which models human belief updating more plausibly than previous models, and a layered agent architecture which allows the simulated agents to make all of their decisions based on the Principle of Parsimony.
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Sotillo, Catherine Frances. "Phonological reduction and intelligibility in task-oriented dialogue." Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/21544.

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This thesis explores the implications of Lindblom's theory of Hyper- and Hypo-articulation (Lindblom, 1983, 1990) for word intelligibility and the likely application of phonological reduction processes in spontaneous discourse, using data from the HCRC Map Task Corpus. Lindblom claims that variability in articulatory clarity is a reflection of speakers' assessments of their listeners' information requirements: speakers hyper-articulate when listeners require maximum acoustic input from with information from other sources. To prevent speakers from over-economising to a point of unintelligibility, hypo-articulation is governed by a constraint of lexical distinctiveness: speakers hypo-articulate only while listeners are able to distinguish the target from competing lexical items. Three main questions are addressed. First, do the informational needs of the listener affect the articulatory clarity of words produced in spontaneous conversation? A series of intelligibility experiments shows that repeated mentions of landmark names are less intelligible than their introductory mentions, independent of which speaker utters either mention, and who can see the landmark on their map. Although the results can be interpreted as supporting Lindblom's view, textual Giveness (Prince, 1981) is shown to depend upon what the speaker knows, rather than what the speaker believes her listener to know. The reduction in clarity associated with an increase in available information is not necessarily listener-oriented as the H & H theory proposes. Secondly, do phonological processes such as word-final /d/-deletion or place assimilation contribute to intelligibility loss? Although reduction processes are found to be more prevalent in tokens from spontaneous discourse than in matched citation forms, they generally fail to account for effects of repetition. An increase in assimilation is found for repeated mentions of nasal-final stimuli in pre-velar position, but no effects is found for assimilation in pre-label position, or for word-final /d/-deletion, nor is an effect found for the duration of schwa in metrically Weak initial syllables of polysyllabic words.
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Books on the topic "Task oriented dialogue system"

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Taboada, María Teresa. Building coherence and cohesion: Task-oriented dialogue in English and Spanish. Philadelphia: John Benjamins Pub. Co., 2004.

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Taboada, María Teresa. Building coherence and cohesion: Task-oriented dialogue in English and Spanish. Amsterdam: J. Benjamins, 2004.

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Taboada, María Teresa. Building coherence and cohesion: Task-oriented dialogue in English and Spanish. Amsterdam: John Benjamins, 2003.

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Bernhaupt, Regina. Human-Centred Software Engineering: Third International Conference, HCSE 2010, Reykjavik, Iceland, October 14-15, 2010. Proceedings. Berlin, Heidelberg: IFIP International Federation of Information Processing, 2010.

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Dadyan, Eduard. Modern programming technologies. The C#language. Volume 2. For advanced users. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1478383.

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The task of volume 2 of the textbook is to describe in detail, in an accessible way, and with practical examples, all the features of the C# language, one of the most promising modern object-oriented programming languages. The course assumes a good command of the material set out in volume 1 of the textbook, and is designed to learn additional features of the C#language. The work with strings, dates and times, threads and the file system, ISON and XML (using practical examples), etc. is considered in detail. The Visual Studio. NET environment is considered as the development environment. All sample programs are given in C#. Meets the requirements of the federal state educational standards of higher education of the latest generation. It is intended for students studying in the direction of training 09.03.03 "Applied Informatics", undergraduate and graduate students of all areas of training and specialties, as well as graduate students and students of the IPK.
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Leech, Geoffrey. Pragmatics and Dialogue. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0007.

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This article introduces the linguistic subdiscipline of pragmatics and shows how this is being applied to the development of spoken dialogue systems — currently perhaps the most important applications area for computational pragmatics. It traces the history of pragmatics from its philosophical roots, and outlines some key notions of theoretical pragmatics — speech acts, illocutionary force, the cooperative principle and relevance. It then discusses the application of pragmatics to dialogue modelling, especially the development of spoken dialogue systems intended to interact with human beings in task-oriented scenarios such as providing travel information and shows how and why computational pragmatics differs from ‘linguistic’ pragmatics, and how pragmatics contributes to the computational analysis of dialogues. One major illustration of this is the application of speech act theory in the analysis and synthesis of service interactions in terms of dialogue acts.
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Watson, Craig. Task-Oriented Performance: The Top Management System. Ballinger Pub Co, 1988.

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Kim, Joong Nam. An agent-based cockpit task management system: A task-oriented pilot-vehicle interface. 1994.

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Kim, Joong Nam. An agent-based cockpit task management system: A task-oriented pilot-vehicle interface. 1994.

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Larson, Victoria M. Task-Oriented, Naturally Elicited Speech (TONE) database for the Force Requirements Expert System, Hawaii (FRESH). 1988.

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Book chapters on the topic "Task oriented dialogue system"

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Peng, Yan, Penghe Chen, Yu Lu, Qinggang Meng, Qi Xu, and Shengquan Yu. "A Task-Oriented Dialogue System for Moral Education." In Lecture Notes in Computer Science, 392–97. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23207-8_72.

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Mazumder, Sahisnu, and Bing Liu. "Continual Learning for Task-Oriented Dialogue Systems." In Synthesis Lectures on Human Language Technologies, 127–51. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-48189-5_6.

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Vogel, Liane, and Lucie Flek. "Investigating Paraphrasing-Based Data Augmentation for Task-Oriented Dialogue Systems." In Text, Speech, and Dialogue, 476–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16270-1_39.

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Berg, Markus M., Antje Düsterhöft, and Bernhard Thalheim. "Towards Interrogative Types in Task-Oriented Dialogue Systems." In Natural Language Processing and Information Systems, 302–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31178-9_38.

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Fang, Ting, Tingting Qiao, and Duanqing Xu. "HaGAN: Hierarchical Attentive Adversarial Learning for Task-Oriented Dialogue System." In Neural Information Processing, 98–109. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36708-4_9.

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Steindl, Sebastian, Ulrich Schäfer, and Bernd Ludwig. "Generating Synthetic Dialogues from Prompts to Improve Task-Oriented Dialogue Systems." In KI 2023: Advances in Artificial Intelligence, 207–14. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42608-7_17.

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Veron, Mathilde, Sahar Ghannay, Anne-Laure Ligozat, and Sophie Rosset. "Lifelong Learning and Task-Oriented Dialogue System: What Does It Mean?" In Lecture Notes in Electrical Engineering, 347–56. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9323-9_32.

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Carbonell, N., and J. M. Pierrel. "Task-Oriented Dialogue Processing in Human-Computer Voice Communication." In Recent Advances in Speech Understanding and Dialog Systems, 491–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-83476-9_46.

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Marques, Tânia. "Towards a Taxonomy of Task-Oriented Domains of Dialogue." In PRIMA 2015: Principles and Practice of Multi-Agent Systems, 510–18. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25524-8_33.

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Chauhan, Anamika, Aditya Malhotra, Anushka Singh, Jwalin Arora, and Shubham Shukla. "Encoding Context in Task-Oriented Dialogue Systems Using Intent, Dialogue Acts, and Slots." In Innovations in Computer Science and Engineering, 287–95. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2043-3_34.

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Conference papers on the topic "Task oriented dialogue system"

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Blache, Philippe. "Dialogue management in task-oriented dialogue systems." In ICMI '17: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3139491.3139507.

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Wei, Zhongyu, Qianlong Liu, Baolin Peng, Huaixiao Tou, Ting Chen, Xuanjing Huang, Kam-fai Wong, and Xiangying Dai. "Task-oriented Dialogue System for Automatic Diagnosis." In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/p18-2033.

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Li, Qian, and Xinmeng Li. "Memory-Augmented Dialogue State Tracker in Task-Oriented Dialogue System." In 2020 International Conference on Computer Information and Big Data Applications (CIBDA). IEEE, 2020. http://dx.doi.org/10.1109/cibda50819.2020.00100.

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Hu, Xiangkun, Junqi Dai, Hang Yan, Yi Zhang, Qipeng Guo, Xipeng Qiu, and Zheng Zhang. "Dialogue Meaning Representation for Task-Oriented Dialogue Systems." In Findings of the Association for Computational Linguistics: EMNLP 2022. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-emnlp.17.

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Xing, Xulei, and Zelong Wang. "Probabilistic Dialogue Model Combined With VAE For Task-oriented Dialogue System." In 2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT). IEEE, 2021. http://dx.doi.org/10.1109/cecit53797.2021.00154.

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Mu, Yanhua, and YiXin Yin. "Task-Oriented Spoken Dialogue System for Humanoid Robot." In 2010 International Conference on Multimedia Technology (ICMT). IEEE, 2010. http://dx.doi.org/10.1109/icmult.2010.5631205.

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Wang, Weizhi, Zhirui Zhang, Junliang Guo, Yinpei Dai, Boxing Chen, and Weihua Luo. "Task-Oriented Dialogue System as Natural Language Generation." In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3477495.3531920.

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Song, Meina, Zhongfu Chen, Peiqing Niu, and E. Haihong. "Task-oriented Dialogue System Based on Reinforcement Learning." In 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2019. http://dx.doi.org/10.1109/icsess47205.2019.9040789.

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Sun, Shaochen, Youlang Ji, Hongying Zhao, and Yang Yu. "Knowledge Graph Driven Dialogue Management for Task-oriented Dialogue." In 2020 International Conference on Intelligent Computing, Automation and Systems (ICICAS). IEEE, 2020. http://dx.doi.org/10.1109/icicas51530.2020.00012.

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Zhao, Xinyan, Bin He, Yasheng Wang, Yitong Li, Fei Mi, Yajiao Liu, Xin Jiang, Qun Liu, and Huanhuan Chen. "UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues." In Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.dialdoc-1.2.

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Reports on the topic "Task oriented dialogue system"

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Traum, David R., and Elizabeth A. Hinkelman. Conversation Acts in Task-Oriented Spoken Dialogue. Fort Belvoir, VA: Defense Technical Information Center, June 1992. http://dx.doi.org/10.21236/ada256368.

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Бакум, З. П., and В. О. Лапіна. Educational Dialogue in the Process of Foreign Language Training of Future Miners. Криворізький державний педагогічний університет, 2014. http://dx.doi.org/10.31812/0564/395.

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On the basis of scientific analysis the article authors develop a scheme that allows planning and organizing the process of learning foreign languages with the use of dialogic didactic means during foreign language training of future miners. The article gives a definition of „educational dialogue‟, observes its structure, and defines its stages: modeling (a future educational dialogue model designing and ways of its implementation at a lesson); motivational (identifying problem, task for solving which encourage further active learnsearch activity of educational dialogue participants); searching (finding out/discovering an effective or new method of problem solving; searching answers to the question); disputing (presenting and discussing results, different positions, viewpoints); concluding (analyzing results, summarizing, substantiating the best chosen way of solving tasks, versions, and opinions). The authors give recommendations for dialogic interaction organizing in the process of forming a foreign professionally oriented speech competence of mining students
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Heredia, Blanca. The Political Economy of Reform of the Administrative Systems of Public Sector Personnel in Latin America: An Analytical Framework. Inter-American Development Bank, November 2002. http://dx.doi.org/10.18235/0012273.

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This study develops a conceptual and analytical reference framework for the preparation of case studies on political determinants of the level of success of the reforms of the system of public-sector personnel in Latin America. Its main goal is to orient the empirical work toward the evaluation of the explanatory weights of the different structural, institutional and strategic variables that have a bearing on the ability of driving forces to initiate and sustain processes of change oriented toward improved efficiency, honesty and responsibility on the part of government employees. This document was commissioned by the Inter-American Development Bank as a Regional Policy Dialogue for the Public Policy Management and Transparency Network's 3rd Meeting on Civil Service Reform held on November 14th and 15th, 2002 in Washington, DC.
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Pinchuk, O. P., and A. A. Prokopenko. Model of a computer-orient-ed methodological system for the development of digital competence of officers of the military administration of the Armed Forces of Ukraine in the system of qualification improvement. Національна академія Державної прикордонної служби України імені Б. Хмельницького, 2023. http://dx.doi.org/10.33407/lib.naes.736836.

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Pedagogical modeling of modern educational environments remains an urgent task of educational sciences. Research on the formation and development of digital competence of specialists, although they have common features, differ and acquire characteristic features depending on the field of application. This is due to the focus on mastering specific professional skills and increasing the professional level. We found out that, compared to the social and humanitarian sphere and medicine, the development of digital competence of specialists in the military and defense industry is little discussed in scientific sources. The development of digital competence of military personnel, in particular military management officers, is an urgent problem that requires an immediate solution. On the one hand, the armed aggression of the Russian Federation adds to the criticality of the situation, on the other hand, scientific and technical progress and, as a result, the appearance of new types of weapons and the complexity of digital tools in the environments of military specialists. Scientific approaches and conceptual principles regarding the formation of digital competence of the Armed Forces of Ukraine and NATO member countries are described. Problems, contradictions and trends in the development of digital education of military specialists in the system of professional development are singled out. The article clarifies the concept of “digital competence of military command officers” of the Armed Forces of Ukraine. The authors developed and substantiated a theoretical model of a computer-oriented methodical system for the development of digital competence of officers of the military administration of the Armed Forces of Ukraine in the system of professional development, which is presented in an informative scheme with a description of individual modules combined into conceptual, target, content-methodical, procedural, technological and effective blocks. The built model ensures systematicity and consistency of the educational process in the digital educational environment of higher military education institutions for the development of digital competencies of military management officers. The technological unit contains a variety of software for training and training. In particular, specialized computer programs and multimedia guides. In the content-methodical block, among other things, the following modules are presented: cloud services; information-didactic and educational-methodical learning tools, multimedia objects, VR/AR tools, AI elements that allow selection of existing ones or creation of new learning materials; Training Course; diagnostic tools, etc. The prospect is the verification of the developed model during distance training.
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Freeman, Paul, Leslie A. Martin, Joanne Linnerooth-Bayer, Reinhard Mechler, Georg Pflug, and Koko Warner. Disaster Risk Management: National Systems for the Comprehensive Management of Disaster Risk and Financial Strategies for Natural Disaster Reconstruction. Inter-American Development Bank, November 2003. http://dx.doi.org/10.18235/0010539.

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This report was commissioned by the Natural Disasters Network of the Regional Policy Dialogue. This report constitutes Phase 2 of this project. While the first phase of the study discusses the components of a national system, the second focuses on instruments for financing reconstruction after a disaster. The research compares centralized, government-directed management systems with those that are localized and decentralized, and also analyzes the factors affecting the financial and political stability of alternative approaches. As natural disasters may result in major resource gaps for governments facing the task of financing reconstruction, the report presents case studies of four countries-Bolivia, Colombia, the Dominican Republic, and El Salvador-to highlight the various policy options. Alternative sources of ex ante funding are identified, including reserve funds, contingent credit, and insurance. These innovative methods of funding are compared with ex post funding possibilities through international aid, loan diversions and increased external debt, budget reallocations, and tax increases.
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Shyshkina, Mariya P. The use of the cloud services to support the math teachers training. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3897.

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The development of the information society and technological progress are significantly influenced by the learning tools. Therefore, to the variety of tools that could be used to support the study of any discipline new ones emerging lately are continuously being added. Along with the great deal of systems of computer mathematics (SCM), web-oriented versions of SCM mathematical applications and other math learning tools the cloud-based versions of mathematical software such as MapleNet, MATLAB web-server, WebMathematica and others are now being used. These tools accomplishment becomes the essential part of training mathematics teachers. Domestic and foreign experiences of using cloud services for forming professional competences of mathematics teachers are analyzed. The place of the CoCalc within the system of mathematical disciplines learning tools is investigated. The task of improving the math teachers’ ICT competence by means of cloud services use in the process of training is considered. Among the new forms of learning rising along with the cloud services dissemination are such as collaborative learning, inquiry-based learning, person-oriented learning. At the same time, the use of the appropriate cloud service in the study of some mathematical discipline improves the assimilation of the learning material and improves the knowledge acquisition process on most topics. The analysis of current research of Ukrainian scientists on the problem in question shows that the progress is underway as for further elaboration and implementation of new learning methods and techniques of using cloud services in the higher education institutions.
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Baader, Franz, and Barbara Morawska. Matching with respect to general concept inclusions in the Description Logic EL. Technische Universität Dresden, 2014. http://dx.doi.org/10.25368/2022.205.

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Matching concept descriptions against concept patterns was introduced as a new inference task in Description Logics (DLs) almost 20 years ago, motivated by applications in the Classic system. For the DL EL, it was shown in 2000 that the matching problem is NP-complete. It then took almost 10 years before this NP-completeness result could be extended from matching to unification in EL. The next big challenge was then to further extend these results from matching and unification without a TBox to matching and unification w.r.t. a general TBox, i.e., a finite set of general concept inclusions. For unification, we could show some partial results for general TBoxes that satisfy a certain restriction on cyclic dependencies between concepts, but the general case is still open. For matching, we solve the general case in this paper: we show that matching in EL w.r.t. general TBoxes is NP-complete by introducing a goal-oriented matching algorithm that uses non-deterministic rules to transform a given matching problem into a solved form by a polynomial number of rule applications. We also investigate some tractable variants of the matching problem.
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