Artículos de revistas: "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 (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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Lu, Hengtong, Caixia Yuan, and Xiaojie Wang. "STOD: Towards Scalable Task-Oriented Dialogue System on MultiWOZ-API." Applied Sciences 14, no. 12 (2024): 5303. http://dx.doi.org/10.3390/app14125303.

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Task-oriented dialogue systems (TODs) enable users to complete specific goals and are widely used in practice. Although existing models have achieved delightful performance for single-domain dialogues, scalability to new domains is far from well explored. Traditional dialogue systems rely on domain-specific information like dialogue state and database (DB), which limits the scalability of such systems. In this paper, we propose a Scalable Task-Oriented Dialogue modeling framework (STOD). Instead of labeling multiple dialogue components, which have been adopted by previous work, we only predict structured API queries to interact with DB and generate responses based on the complete DB results. Further, we construct a new API-schema-based TOD dataset MultiWOZ-API with API query and DB result annotation based on MultiWOZ 2.1. We then propose MSTOD and CSTOD for multi-domain and cross-domain TOD systems, respectively. We perform extensive qualitative experiments to verify the effectiveness of our proposed framework. We find the following. (1) Scalability across multiple domains: MSTOD achieves 2% improvements than the previous state-of-the-art in the multi-domain TOD. (2) Scalability to new domains: our framework enables satisfying generalization capability to new domains, a significant margin of 10% to existing baselines.

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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 (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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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 (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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Ipinnimo, O., T. Adegbola, C. O. Folorunso та M. Isu. "SỌ̀RỌ̀: A Yorùbá Language Task Oriented Dialogue System". Journal of Engineering Research 27, № 1 (2022): 26–38. https://doi.org/10.52968/72016307.

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This paper presents Sọ̀rọ̀: a task-oriented dialogue system, developed utilising a rule-based technique. Sọ̀rọ̀ is fit for having a trade of four key sorts: greetings, small talk, basic number-crunching, and time/date. This dialogue framework has been fabricated following a linguistic investigation of the Yorùbá language and the rules defined from the analysis of publicly supported information. Sọ̀rọ̀'s conversational capacities are restricted to text-based trade and revolved only around a small domain of topics due to its limited vocabulary data sets. The framework involves three primary and auxiliary scripts each. The primary scripts are the bag-of-words, natural language understanding and natural language processing scripts while the auxiliary scripts are the task manager, the properties script, and the main script where dialogue sessions occur. This study developed rules that identify sentence types in utterances, split sentences into intent and entity, perform a list of tasks as identified in utterances and provide a response to this effect. This paper characterises rules that relate 66.7% accuracy the type of sentence contained within a sample utterance, with a precision of 94.7%. This study demonstrates the practicality of a Yorùbá language dialogue framework and simultaneously, design a dynamic dialogue system architecture likely to be improved upon with additional data.

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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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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 (2019): 1–30. http://dx.doi.org/10.1145/3317612.

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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 (2022): 1481–89. http://dx.doi.org/10.1587/transinf.2022edp7001.

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Fuad, Ahlam, and Maha Al-Yahya. "AraConv: Developing an Arabic Task-Oriented Dialogue System Using Multi-Lingual Transformer Model mT5." Applied Sciences 12, no. 4 (2022): 1881. http://dx.doi.org/10.3390/app12041881.

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Task-oriented dialogue systems (DS) are designed to help users perform daily activities using natural language. Task-oriented DS for English language have demonstrated promising performance outcomes; however, developing such systems to support Arabic remains a challenge. This challenge is mainly due to the lack of Arabic dialogue datasets. This study introduces the first Arabic end-to-end generative model for task-oriented DS (AraConv), which uses the multi-lingual transformer model mT5 with different settings. We also present an Arabic dialogue dataset (Arabic-TOD) and used it to train and test the proposed AraConv model. The results obtained are reasonable compared to those reported in the studies of English and Chinese using the same mono-lingual settings. To avoid problems associated with a small training dataset and to improve the AraConv model’s results, we suggest joint-training, in which the model is jointly trained on Arabic dialogue data and data from one or two high-resource languages such as English and Chinese. The findings indicate the AraConv model performed better in the joint-training setting than in the mono-lingual setting. The results obtained from AraConv on the Arabic dialogue dataset provide a baseline for other researchers to build robust end-to-end Arabic task-oriented DS that can engage with complex scenarios.

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Firdaus, Mauajama, Nidhi Thakur, and Asif Ekbal. "Aspect-Aware Response Generation for Multimodal Dialogue System." ACM Transactions on Intelligent Systems and Technology 12, no. 2 (2021): 1–33. http://dx.doi.org/10.1145/3430752.

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Multimodality in dialogue systems has opened up new frontiers for the creation of robust conversational agents. Any multimodal system aims at bridging the gap between language and vision by leveraging diverse and often complementary information from image, audio, and video, as well as text. For every task-oriented dialog system, different aspects of the product or service are crucial for satisfying the user’s demands. Based upon the aspect, the user decides upon selecting the product or service. The ability to generate responses with the specified aspects in a goal-oriented dialogue setup facilitates user satisfaction by fulfilling the user’s goals. Therefore, in our current work, we propose the task of aspect controlled response generation in a multimodal task-oriented dialog system. We employ a multimodal hierarchical memory network for generating responses that utilize information from both text and images. As there was no readily available data for building such multimodal systems, we create a Multi-Domain Multi-Modal Dialog (MDMMD++) dataset. The dataset comprises the conversations having both text and images belonging to the four different domains, such as hotels, restaurants, electronics, and furniture. Quantitative and qualitative analysis on the newly created MDMMD++ dataset shows that the proposed methodology outperforms the baseline models for the proposed task of aspect controlled response generation.

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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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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 (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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Lao, Yadi, Weijie Liu, Sheng Gao, and Si Li. "MDKB-Bot: A Practical Framework for Multi-Domain Task-Oriented Dialogue System." Data Intelligence 1, no. 2 (2019): 176–86. http://dx.doi.org/10.1162/dint_a_00010.

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One of the major challenges to build a task-oriented dialogue system is that dialogue state transition frequently happens between multiple domains such as booking hotels or restaurants. Recently, the encoderdecoder model based on the end-to-end neural network has become an attractive approach to meet this challenge. However, it usually requires a sufficiently large amount of training data and it is not flexible to handle dialogue state transition. This paper addresses these problems by proposing a simple but practical framework called Multi-Domain KB-BOT (MDKB-BOT), which leverages both neural networks and rule-based strategy in natural language understanding (NLU) and dialogue management (DM). Experiments on the data set of the Chinese Human-Computer Dialogue Technology Evaluation Campaign show that MDKB-BOT achieves competitive performance on several evaluation metrics, including task completion rate and user satisfaction.

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Xu, Qiancheng, Min Yang, and Binzong Geng. "Class Incremental Learning for Task-Oriented Dialogue System with Contrastive Distillation on Internal Representations (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16368–69. http://dx.doi.org/10.1609/aaai.v37i13.27044.

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The ability to continually learn over time by grasping new knowledge and remembering previously learned experiences is essential for developing an online task-oriented dialogue system (TDS). In this paper, we work on the class incremental learning scenario where the TDS is evaluated without specifying the dialogue domain. We employ contrastive distillation on the intermediate representations of dialogues to learn transferable representations that suffer less from catastrophic forgetting. Besides, we provide a dynamic update mechanism to explicitly preserve the learned experiences by only updating the parameters related to the new task while keeping other parameters fixed. Extensive experiments demonstrate that our method significantly outperforms the strong baselines.

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MATSUI, Tatsuya, and Masafumi HAGIWARA. "Non-task-oriented Dialogue System with Humor Considering Utterances Polarity." Transactions of Japan Society of Kansei Engineering 14, no. 1 (2015): 9–16. http://dx.doi.org/10.5057/jjske.14.9.

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Liu, Mengjuan, Jiang Liu, Chenyang Liu, and Kuo-Hui Yeh. "A task-oriented neural dialogue system capable of knowledge accessing." Journal of Information Security and Applications 76 (August 2023): 103551. http://dx.doi.org/10.1016/j.jisa.2023.103551.

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Li, Kuo-Chen, Maiga Chang, and Kuan-Hsing Wu. "Developing a Task-Based Dialogue System for English Language Learning." Education Sciences 10, no. 11 (2020): 306. http://dx.doi.org/10.3390/educsci10110306.

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This research involved the design of a task-based dialogue system and evaluation of its learning effectiveness. Dialogue training still heavily depends on human communication with instant feedback or correction. However, it is not possible to provide a personal tutor for every English learner. With the rapid development of information technology, digitized learning and voice communication is a possible solution. The goal of this research was to develop an innovative model to refine the task-based dialogue system, including natural language understanding, disassembly intention, and dialogue state tracking. To enable the dialogue system to find the corresponding sentence accurately, the dialogue system was designed with machine learning algorithms to allow users to communicate in a task-based fashion. Past research has pointed out that computer-assisted instruction has achieved remarkable results in language reading, writing, and listening. Therefore, the direction of the discussion is to use the task-oriented dialogue system as a speaking teaching assistant. To train the speaking ability, the proposed system provides a simulation environment with goal-oriented characteristics, allowing learners to continuously improve their language fluency in terms of speaking ability by simulating conversational situational exercises. To evaluate the possibility of replacing the traditional English speaking practice with the proposed system, a small English speaking class experiment was carried out to validate the effectiveness of the proposed system. Data of 28 students with three assigned tasks were collected and analyzed. The promising results of the collected students’ feedback confirm the positive perceptions toward the system regarding user interface, learning style, and the system’s effectiveness.

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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 (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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Kang, Hoon-chul, Myeong-Cheol Jwa, and Jeong-Woo Jwa. "The task-oriented Smart Tourism Chatbot Service." International Journal of Membrane Science and Technology 10, no. 4 (2023): 235–43. http://dx.doi.org/10.15379/ijmst.v10i4.1888.

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The smart tourism service provides tourists with a travel planner service before the trip and a tour guide service during the trip. Smart tourism chatbot service can easily and conveniently provide smart tourism services to tourists along with smart tourism apps. In this paper, we develop the smart tourism platform and propose the task-oriented smart tourism chatbot service that efficiently provides tourism information provided by smart tourism apps to users. The smart tourism platform consists of the smart tourism chatbot and smart tourism information systems. The smart tourism chatbot system identifies the intention of the user's question and searches for the tourism information ID in the tourism information knowledgebase. The smart tourism information system provides tourism information of the smart tourism app to the user using the searched tourism information ID. The proposed smart tourism chatbot system applies the tourism information NER (Named Entity Recognition) model to accurately understand the intention of the user's question to the existing DST (Dialogue State Tracking) model-based chatbot system. We create tourist information named entities of the NER model based on the values of the domain and slot in the DST model defined in the 4W1H method. The NER model determines the domain, slot, and value to identify the intention of the user's question, and the DST model manages the dialogue state and retrieves the tourism information ID from the tourism information knowledgebase.

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Ayedoun, Emmanuel, Yuki Hayashi, and Kazuhisa Seta. "An authoring tool for task-oriented dialogue scenarios design in EFL context." Research and Practice in Technology Enhanced Learning 18 (December 28, 2022): 027. http://dx.doi.org/10.58459/rptel.2023.18027.

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Computer-based conversational environments have been advocated as a promising approach for providing virtual, yet realistic opportunities for communication practice to second language learners. However, the high authoring costs of such environments continue to prevent their widespread diffusion and adoption. Furthermore, there is a limited set of authoring interfaces dedicated to making the creation of dialogue scenarios in the context of language learning easier. In this research, we present a dialogue scenario authoring system that could aid the rapid implementation of desirable dialogue scenarios, lowering the barrier to dialogue scenario authoring for non-programmers or even educators. To that end, we built a pseudo-versatile dialogue scenario authoring interface that enables the automatic generation of services-related dialogue scenarios by leveraging the common underlying structure of services (restaurant, hotel, travel planning, etc.) that appear to share a certain degree of similarity at the task level. Here, we describe the proposed system’s features and present the findings of an experimental evaluation study that suggests the usefulness of our approach to facilitating dialogue scenarios designed by people with no prior experience authoring dialogue systems components. According to an evaluation of the tool by a second language teaching expert, the proposed system might also foster second language teaching and learning by allowing both educators and learners to participate in the design of dialogue scenarios that are adapted to different levels of learners.

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Fang Gao, Weilin Liu, Chengli Wang, and Yanhua Zhang. "Application of task-oriented dialogue in operations for future networks to business." ITU Journal on Future and Evolving Technologies 5, no. 4 (2024): 515–23. https://doi.org/10.52953/sxax7848.

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At present, the network operation services provided by operators for to business (toB) customers have customer friendliness issues mostly affected by manual input. The limitations are reflected in four aspects: time consumption and understanding differences in the process of customer intention communication, waiting time and lack of process transparency. Therefore, in order to provide a more automatic, standard, concise and user-engaged method on operations for future networks in the toB field, this paper proposes applying a task-oriented dialogue system to the network operation task processing. The most significant advantage is that it makes possible for industry customers to participate directly in and facilitate their task completion process together with the system by several turns of dialogue. The conventional pipeline structure which combines both neural networks and rules is appropriate for meeting the above requirements for providing unified natural language understanding of intent, tracking dialogue state and generating actions of reply or command. With the help of a knowledge database and a network management system, the dialogue system provides users with more detailed operation information, which directly promotes user participation and transparency of the whole service process.

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Mu, Yan Hua. "Task-Oriented Architecture for a Humanoid Robot." Applied Mechanics and Materials 40-41 (November 2010): 228–34. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.228.

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A humanoid robot is developed to perform various kinds of tasks in human daily environments. In this paper, three advanced capabilities which are indispensable for a humanoid robot to perform tasks, namely, natural human-humanoid robot interaction based on spoken dialogue and vision, vision-based navigation in complex and dynamic environments, object grasp and manipulation with hand-eye coordination, are discussed firstly. Then, a biologically-inspired system structure for a humanoid robot is presented. Based on this system structure, a task-oriented layered architecture for a humanoid robot is proposed, and the architectures of the three advanced capabilities are presented, respectively.

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Kobyashi, Shunya, and Masafumi Hagiwara. "Non-task-oriented Dialogue System Considering User's Preference and Human Relations." Transactions of the Japanese Society for Artificial Intelligence 31, no. 1 (2016): DSF—A_1–10. http://dx.doi.org/10.1527/tjsai.dsf-502.

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Kim, Jinyoung, Hyunmook Cha, and Youngjoong Ko. "Task-Oriented Dialogue System Using a Fusion Module between Knowledge Graphs." Journal of KIISE 51, no. 10 (2024): 882–91. http://dx.doi.org/10.5626/jok.2024.51.10.882.

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Qin, Libo, Zhouyang Li, Qiying Yu, Lehan Wang, and Wanxiang Che. "Towards Complex Scenarios: Building End-to-End Task-Oriented Dialogue System across Multiple Knowledge Bases." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13483–91. http://dx.doi.org/10.1609/aaai.v37i11.26581.

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With the success of the sequence-to-sequence model, end-to-end task-oriented dialogue systems (EToDs) have obtained remarkable progress. However, most existing EToDs are limited to single KB settings where dialogues can be supported by a single KB, which is still far from satisfying the requirements of some complex applications (multi-KBs setting). In this work, we first empirically show that the existing single-KB EToDs fail to work on multi-KB settings that require models to reason across various KBs. To solve this issue, we take the first step to consider the multi-KBs scenario in EToDs and introduce a KB-over-KB Heterogeneous Graph Attention Network (KoK-HAN) to facilitate model to reason over multiple KBs. The core module is a triple-connection graph interaction layer that can model different granularity levels of interaction information across different KBs (i.e., intra-KB connection, inter-KB connection and dialogue-KB connection). Experimental results confirm the superiority of our model for multiple KBs reasoning.

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Kim, June-Woo, Hyekyung Yoon, and Ho-Young Jung. "Improved Spoken Language Representation for Intent Understanding in a Task-Oriented Dialogue System." Sensors 22, no. 4 (2022): 1509. http://dx.doi.org/10.3390/s22041509.

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Successful applications of deep learning technologies in the natural language processing domain have improved text-based intent classifications. However, in practical spoken dialogue applications, the users’ articulation styles and background noises cause automatic speech recognition (ASR) errors, and these may lead language models to misclassify users’ intents. To overcome the limited performance of the intent classification task in the spoken dialogue system, we propose a novel approach that jointly uses both recognized text obtained by the ASR model and a given labeled text. In the evaluation phase, only the fine-tuned recognized language model (RLM) is used. The experimental results show that the proposed scheme is effective at classifying intents in the spoken dialogue system containing ASR errors.

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Wang, Lifang, Meng Zhao, Hongru Ji, et al. "Dialogue summarization enhanced response generation for multi-domain task-oriented dialogue systems." Information Processing & Management 61, no. 3 (2024): 103668. http://dx.doi.org/10.1016/j.ipm.2024.103668.

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Mo, Yunho, Joon Yoo, and Sangwoo Kang. "Parameter-Efficient Fine-Tuning Method for Task-Oriented Dialogue Systems." Mathematics 11, no. 14 (2023): 3048. http://dx.doi.org/10.3390/math11143048.

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The use of Transformer-based pre-trained language models has become prevalent in enhancing the performance of task-oriented dialogue systems. These models, which are pre-trained on large text data to grasp the language syntax and semantics, fine-tune the entire parameter set according to a specific task. However, as the scale of the pre-trained language model increases, several challenges arise during the fine-tuning process. For example, the training time escalates as the model scale grows, since the complete parameter set needs to be trained. Furthermore, additional storage space is required to accommodate the larger model size. To address these challenges, we propose a new new task-oriented dialogue system called PEFTTOD. Our proposal leverages a method called the Parameter-Efficient Fine-Tuning method (PEFT), which incorporates an Adapter Layer and prefix tuning into the pre-trained language model. It significantly reduces the overall parameter count used during training and efficiently transfers the dialogue knowledge. We evaluated the performance of PEFTTOD on the Multi-WOZ 2.0 dataset, a benchmark dataset commonly used in task-oriented dialogue systems. Compared to the traditional method, PEFTTOD utilizes only about 4% of the parameters for training, resulting in a 4% improvement in the combined score compared to the existing T5-based baseline. Moreover, PEFTTOD achieved an efficiency gain by reducing the training time by 20% and saving up to 95% of the required storage space.

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Mo, Yunho, and Sangwoo Kang. "Adapter-based Learning Methods for Task-oriented Dialogue Systems." Journal of Digital Contents Society 24, no. 6 (2023): 1221–28. http://dx.doi.org/10.9728/dcs.2023.24.6.1221.

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Yoshikawa, Tomohiro, and Ryosuke Iwakura. "Study on Development of Humor Discriminator for Dialogue System." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 3 (2020): 422–35. http://dx.doi.org/10.20965/jaciii.2020.p0422.

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Studies on automatic dialogue systems, which allow people and computers to communicate with each other using natural language, have been attracting attention. In particular, the main objective of a non-task-oriented dialogue system is not to achieve a specific task but to amuse users through chat and free dialogue. For this type of dialogue system, continuity of the dialogue is important because users can easily get tired if the dialogue is monotonous. On the other hand, preceding studies have shown that speech with humorous expressions is effective in improving the continuity of a dialogue. In this study, we developed a computer-based humor discriminator to perform user- or situation-independent objective discrimination of humor. Using the humor discriminator, we also developed an automatic humor generation system and conducted an evaluation experiment with human subjects to test the generated jokes. A t-test on the evaluation scores revealed a significant difference (P value: 3.5×10-5) between the proposed and existing methods of joke generation.

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31

Firdaus, Mauajama, Arunav Pratap Shandeelya, and Asif Ekbal. "More to diverse: Generating diversified responses in a task oriented multimodal dialog system." PLOS ONE 15, no. 11 (2020): e0241271. http://dx.doi.org/10.1371/journal.pone.0241271.

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Multimodal dialogue system, due to its many-fold applications, has gained much attention to the researchers and developers in recent times. With the release of large-scale multimodal dialog dataset Saha et al. 2018 on the fashion domain, it has been possible to investigate the dialogue systems having both textual and visual modalities. Response generation is an essential aspect of every dialogue system, and making the responses diverse is an important problem. For any goal-oriented conversational agent, the system’s responses must be informative, diverse and polite, that may lead to better user experiences. In this paper, we propose an end-to-end neural framework for generating varied responses in a multimodal dialogue setup capturing information from both the text and image. Multimodal encoder with co-attention between the text and image is used for focusing on the different modalities to obtain better contextual information. For effective information sharing across the modalities, we combine the information of text and images using the BLOCK fusion technique that helps in learning an improved multimodal representation. We employ stochastic beam search with Gumble Top K-tricks to achieve diversified responses while preserving the content and politeness in the responses. Experimental results show that our proposed approach performs significantly better compared to the existing and baseline methods in terms of distinct metrics, and thereby generates more diverse responses that are informative, interesting and polite without any loss of information. Empirical evaluation also reveals that images, while used along with the text, improve the efficiency of the model in generating diversified responses.

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32

Li, Shimin, Qinyuan Cheng, Linyang Li, and Xipeng Qiu. "Mitigating Negative Style Transfer in Hybrid Dialogue System." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13103–11. http://dx.doi.org/10.1609/aaai.v37i11.26539.

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As the functionality of dialogue systems evolves, hybrid dialogue systems that accomplish user-specific goals and participate in open-topic chitchat with users are attracting growing attention. Existing research learns both tasks concurrently utilizing a multi-task fusion technique but ignores the negative transfer phenomenon induced by the unique textual style differences. Therefore, contrastive learning based on the latent variable model is used to decouple the various textual genres in the latent space. We devise supervised and self-supervised positive and negative sample constructions for diverse datasets. In addition, to capitalize on the style information contained in the decoupled latent variables, we employ a style prefix that incorporates latent variables further to control the generation of responses with varying styles. We performed extensive experiments on three dialogue datasets, including a hybrid dialogue dataset and two task-oriented dialogue datasets. The experimental results demonstrate that our method can mitigate the negative style transfer issue and achieves state-of-the-art performance on multiple dialogue datasets.

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33

Jeon, Hyunmin, and Gary Geunbae Lee. "DORA: Towards policy optimization for task-oriented dialogue system with efficient context." Computer Speech & Language 72 (March 2022): 101310. http://dx.doi.org/10.1016/j.csl.2021.101310.

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34

Paek, Ellie S., Talyn Fan, James D. Finch, and Jinho D. Choi. "Enhancing Task-Oriented Dialogue Systems through Synchronous Multi-Party Interaction and Multi-Group Virtual Simulation." Information 15, no. 9 (2024): 580. http://dx.doi.org/10.3390/info15090580.

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This paper presents two innovative approaches: a synchronous multi-party dialogue system that engages in simultaneous interactions with multiple users, and multi-group simulations involving virtual user groups to evaluate the resilience of this system. Unlike most other chatbots that communicate with each user independently, our system facilitates information gathering from multiple users and executes 17 administrative tasks for group requests adeptly by leveraging a state machine-based framework for complete control over dialogue flow and a large language model (LLM) for robust context understanding. Assessing such a unique dialogue system poses challenges, as it requires many groups of users to interact with the system concurrently for an extended duration. To address this, we simulate various virtual groups using an LLM, each comprising 10–30 users who may belong to multiple groups, in order to evaluate the efficacy of our system; each user is assigned a persona and allowed to interact freely without scripts. As a result, our system shows average success rates of 87% for task completion and 89% for natural language understanding. Comparatively, our virtual simulation, which has an average success rate of 80%, is juxtaposed with a group of 15 human users, depicting similar task diversity and error trends. To our knowledge, it is the first work to show the LLM’s potential in both task execution and the simulation of a synchronous dialogue system to fully automate administrative tasks.

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35

Lin, Jessy, Nicholas Tomlin, Jacob Andreas, and Jason Eisner. "Decision-Oriented Dialogue for Human-AI Collaboration." Transactions of the Association for Computational Linguistics 12 (2024): 892–911. http://dx.doi.org/10.1162/tacl_a_00679.

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Abstract We describe a class of tasks called decision-oriented dialogues, in which AI assistants such as large language models (LMs) must collaborate with one or more humans via natural language to help them make complex decisions. We formalize three domains in which users face everyday decisions: (1) choosing an assignment of reviewers to conference papers, (2) planning a multi-step itinerary in a city, and (3) negotiating travel plans for a group of friends. In each of these settings, AI assistants and users have disparate abilities that they must combine to arrive at the best decision: Assistants can access and process large amounts of information, while users have preferences and constraints external to the system. For each task, we build a dialogue environment where agents receive a reward based on the quality of the final decision they reach. We evaluate LMs in self-play and in collaboration with humans and find that they fall short compared to human assistants, achieving much lower rewards despite engaging in longer dialogues. We highlight a number of challenges models face in decision-oriented dialogues, ranging from goal-directed behavior to reasoning and optimization, and release our environments as a testbed for future work.

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36

Ohashi, Atsumoto, and Ryuichiro Higashinaka. "Universal Post-Processing Networks for Joint Optimization of Modules in Task-Oriented Dialogue Systems." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24975–83. https://doi.org/10.1609/aaai.v39i23.34681.

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Post-processing networks (PPNs) are components that modify the outputs of arbitrary modules in task-oriented dialogue systems and are optimized using reinforcement learning (RL) to improve the overall task completion capability of the system. However, previous PPN-based approaches have been limited to handling only a subset of modules within a system, which poses a significant limitation in improving the system performance. In this study, we propose a joint optimization method for post-processing the outputs of all modules using universal post-processing networks (UniPPNs), which are language-model-based networks that can modify the outputs of arbitrary modules in a system as a sequence-transformation task. Moreover, our RL algorithm, which employs a module-level Markov decision process, enables fine-grained value and advantage estimation for each module, thereby stabilizing joint learning for post-processing the outputs of all modules. Through both simulation-based and human evaluation experiments using the MultiWOZ dataset, we demonstrated that UniPPN outperforms conventional PPNs in the task completion capability of task-oriented dialogue systems.

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37

Rastogi, Abhinav, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, and Pranav Khaitan. "Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided Dialogue Dataset." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8689–96. http://dx.doi.org/10.1609/aaai.v34i05.6394.

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Virtual assistants such as Google Assistant, Alexa and Siri provide a conversational interface to a large number of services and APIs spanning multiple domains. Such systems need to support an ever-increasing number of services with possibly overlapping functionality. Furthermore, some of these services have little to no training data available. Existing public datasets for task-oriented dialogue do not sufficiently capture these challenges since they cover few domains and assume a single static ontology per domain. In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains. Our dataset exceeds the existing task-oriented dialogue corpora in scale, while also highlighting the challenges associated with building large-scale virtual assistants. It provides a challenging testbed for a number of tasks including language understanding, slot filling, dialogue state tracking and response generation. Along the same lines, we present a schema-guided paradigm for task-oriented dialogue, in which predictions are made over a dynamic set of intents and slots, provided as input, using their natural language descriptions. This allows a single dialogue system to easily support a large number of services and facilitates simple integration of new services without requiring additional training data. Building upon the proposed paradigm, we release a model for dialogue state tracking capable of zero-shot generalization to new APIs, while remaining competitive in the regular setting.

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38

Choi, Sung-Kwon, Yo-Han Lee, and Oh-Wook Kwon. "A study on task-oriented dialogue data of a dialogue system for foreign language tutoring: Focusing on Korean dialogue data." Foreign Languages Education 29, no. 1 (2022): 105–24. http://dx.doi.org/10.15334/fle.2022.29.1.105.

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39

Lee, Hayoung, and Okran Jeong. "A Knowledge-Grounded Task-Oriented Dialogue System with Hierarchical Structure for Enhancing Knowledge Selection." Sensors 23, no. 2 (2023): 685. http://dx.doi.org/10.3390/s23020685.

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For a task-oriented dialogue system to provide appropriate answers to and services for users’ questions, it is necessary for it to be able to utilize knowledge related to the topic of the conversation. Therefore, the system should be able to select the most appropriate knowledge snippet from the knowledge base, where external unstructured knowledge is used to respond to user requests that cannot be solved by the internal knowledge addressed by the database or application programming interface. Therefore, this paper constructs a three-step knowledge-grounded task-oriented dialogue system with knowledge-seeking-turn detection, knowledge selection, and knowledge-grounded generation. In particular, we propose a hierarchical structure of domain-classification, entity-extraction, and snippet-ranking tasks by subdividing the knowledge selection step. Each task is performed through the pre-trained language model with advanced techniques to finally determine the knowledge snippet to be used to generate a response. Furthermore, the domain and entity information obtained because of the previous task is used as knowledge to reduce the search range of candidates, thereby improving the performance and efficiency of knowledge selection and proving it through experiments.

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40

Ding, Zeyuan, Zhihao Yang, Yinbo Qiao, and Hongfei Lin. "KMc-ToD: Structure knowledge enhanced multi-copy network for task-oriented dialogue system." Knowledge-Based Systems 293 (June 2024): 111662. http://dx.doi.org/10.1016/j.knosys.2024.111662.

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41

Xu, Qiancheng, Min Yang, and Ruifeng Xu. "Balanced Meta Learning and Diverse Sampling for Lifelong Task-Oriented Dialogue Systems." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13843–52. http://dx.doi.org/10.1609/aaai.v37i11.26621.

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In real-world scenarios, it is crucial to build a lifelong taskoriented dialogue system (TDS) that continually adapts to new knowledge without forgetting previously acquired experiences. Existing approaches mainly focus on mitigating the catastrophic forgetting in lifelong TDS. However, the transfer ability to generalize the accumulated old knowledge to new tasks is underexplored. In this paper, we propose a two-stage lifelong task-oriented dialogue generation method to mitigate catastrophic forgetting and encourage knowledge transfer simultaneously, inspired by the learning process. In the first stage, we learn task-specific masks which adaptively preserve the knowledge of each visited task so as to mitigate catastrophic forgetting. In this stage, we are expected to learn the task-specific knowledge which is tailored for each task. In the second stage, we bring the knowledge from the encountered tasks together and understand thoroughly. To this end, we devise a balanced meta learning strategy for both forward and backward knowledge transfer in the lifelong learning process. In particular, we perform meta-update with a meta-test set sampled from the current training data for forward knowledge transfer. In addition, we employ an uncertainty-based sampling strategy to select and store representative dialogue samples into episodic memory and perform meta-update with a meta-test set sampled from the memory for backward knowledge transfer. With extensive experiments on 29 tasks, we show that MetaLTDS outperforms the strong baselines in terms of both effectiveness and efficiency. For reproducibility, we submit our code at: https: //github.com/travis-xu/MetaLTDS.

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42

Zhou, Yangyang, and Fuji Ren. "CERG: Chinese Emotional Response Generator with Retrieval Method." Research 2020 (September 7, 2020): 1–8. http://dx.doi.org/10.34133/2020/2616410.

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The dialogue system has always been one of the important topics in the domain of artificial intelligence. So far, most of the mature dialogue systems are task-oriented based, while non-task-oriented dialogue systems still have a lot of room for improvement. We propose a data-driven non-task-oriented dialogue generator “CERG” based on neural networks. This model has the emotion recognition capability and can generate corresponding responses. The data set we adopt comes from the NTCIR-14 STC-3 CECG subtask, which contains more than 1.7 million Chinese Weibo post-response pairs and 6 emotion categories. We try to concatenate the post and the response with the emotion, then mask the response part of the input text character by character to emulate the encoder-decoder framework. We use the improved transformer blocks as the core to build the model and add regularization methods to alleviate the problems of overcorrection and exposure bias. We introduce the retrieval method to the inference process to improve the semantic relevance of generated responses. The results of the manual evaluation show that our proposed model can make different responses to different emotions to improve the human-computer interaction experience. This model can be applied to lots of domains, such as automatic reply robots of social application.

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43

Hassan, Javeria, Muhammad Ali Tahir, and Adnan Ali. "Natural language understanding of map navigation queries in Roman Urdu by joint entity and intent determination." PeerJ Computer Science 7 (July 21, 2021): e615. http://dx.doi.org/10.7717/peerj-cs.615.

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Navigation based task-oriented dialogue systems provide users with a natural way of communicating with maps and navigation software. Natural language understanding (NLU) is the first step for a task-oriented dialogue system. It extracts the important entities (slot tagging) from the user’s utterance and determines the user’s objective (intent determination). Word embeddings are the distributed representations of the input sentence, and encompass the sentence’s semantic and syntactic representations. We created the word embeddings using different methods like FastText, ELMO, BERT and XLNET; and studied their effect on the natural language understanding output. Experiments are performed on the Roman Urdu navigation utterances dataset. The results show that for the intent determination task XLNET based word embeddings outperform other methods; while for the task of slot tagging FastText and XLNET based word embeddings have much better accuracy in comparison to other approaches.

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44

Liu, Zihan, Genta Indra Winata, Zhaojiang Lin, Peng Xu, and Pascale Fung. "Attention-Informed Mixed-Language Training for Zero-Shot Cross-Lingual Task-Oriented Dialogue Systems." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8433–40. http://dx.doi.org/10.1609/aaai.v34i05.6362.

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Recently, data-driven task-oriented dialogue systems have achieved promising performance in English. However, developing dialogue systems that support low-resource languages remains a long-standing challenge due to the absence of high-quality data. In order to circumvent the expensive and time-consuming data collection, we introduce Attention-Informed Mixed-Language Training (MLT), a novel zero-shot adaptation method for cross-lingual task-oriented dialogue systems. It leverages very few task-related parallel word pairs to generate code-switching sentences for learning the inter-lingual semantics across languages. Instead of manually selecting the word pairs, we propose to extract source words based on the scores computed by the attention layer of a trained English task-related model and then generate word pairs using existing bilingual dictionaries. Furthermore, intensive experiments with different cross-lingual embeddings demonstrate the effectiveness of our approach. Finally, with very few word pairs, our model achieves significant zero-shot adaptation performance improvements in both cross-lingual dialogue state tracking and natural language understanding (i.e., intent detection and slot filling) tasks compared to the current state-of-the-art approaches, which utilize a much larger amount of bilingual data.

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45

Jordan, P. W., and M. A. Walker. "Learning Content Selection Rules for Generating Object Descriptions in Dialogue." Journal of Artificial Intelligence Research 24 (July 1, 2005): 157–94. http://dx.doi.org/10.1613/jair.1591.

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A fundamental requirement of any task-oriented dialogue system is the ability to generate object descriptions that refer to objects in the task domain. The subproblem of content selection for object descriptions in task-oriented dialogue has been the focus of much previous work and a large number of models have been proposed. In this paper, we use the annotated COCONUT corpus of task-oriented design dialogues to develop feature sets based on Dale and Reiter's (1995) incremental model, Brennan and Clark's (1996) conceptual pact model, and Jordan's (2000b) intentional influences model, and use these feature sets in a machine learning experiment to automatically learn a model of content selection for object descriptions. Since Dale and Reiter's model requires a representation of discourse structure, the corpus annotations are used to derive a representation based on Grosz and Sidner's (1986) theory of the intentional structure of discourse, as well as two very simple representations of discourse structure based purely on recency. We then apply the rule-induction program RIPPER to train and test the content selection component of an object description generator on a set of 393 object descriptions from the corpus. To our knowledge, this is the first reported experiment of a trainable content selection component for object description generation in dialogue. Three separate content selection models that are based on the three theoretical models, all independently achieve accuracies significantly above the majority class baseline (17%) on unseen test data, with the intentional influences model (42.4%) performing significantly better than either the incremental model (30.4%) or the conceptual pact model (28.9%). But the best performing models combine all the feature sets, achieving accuracies near 60%. Surprisingly, a simple recency-based representation of discourse structure does as well as one based on intentional structure. To our knowledge, this is also the first empirical comparison of a representation of Grosz and Sidner's model of discourse structure with a simpler model for any generation task.

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46

Li, Yangming, and Kaisheng Yao. "Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 15 (2021): 13306–14. http://dx.doi.org/10.1609/aaai.v35i15.17571.

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End-to-end neural networks have achieved promising performances in natural language generation (NLG). However, they are treated as black boxes and lack interpretability. To address this problem, we propose a novel framework, heterogeneous rendering machines (HRM), that interprets how neural generators render an input dialogue act (DA) into an utterance. HRM consists of a renderer set and a mode switcher. The renderer set contains multiple decoders that vary in both structure and functionality. For every generation step, the mode switcher selects an appropriate decoder from the renderer set to generate an item (a word or a phrase). To verify the effectiveness of our method, we have conducted extensive experiments on 5 benchmark datasets. In terms of automatic metrics (e.g., BLEU), our model is competitive with the current state-of-the-art method. The qualitative analysis shows that our model can interpret the rendering process of neural generators well. Human evaluation also confirms the interpretability of our proposed approach.

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47

Lee, un-Beom, So-Eon Kim, and Seong-Bae Park. "Adaptive Knowledge Transfer for Continual Learning of Task-Oriented Dialogue Systems." KIISE Transactions on Computing Practices 30, no. 11 (2024): 606–10. https://doi.org/10.5626/ktcp.2024.30.11.606.

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48

Sun, Jingtao, Jiayin Kou, Wenyan Hou, and Yujei Bai. "A multi-agent curiosity reward model for task-oriented dialogue systems." Pattern Recognition 157 (January 2025): 110884. http://dx.doi.org/10.1016/j.patcog.2024.110884.

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49

Miyamoto, Tomoki, Nozomu Nagai, Yuto Mitsuta, et al. "A Proposal and Implement in Non-task-oriented Dialogue System of Risky Politeness Strategy." Transactions of the Japanese Society for Artificial Intelligence 37, no. 3 (2022): IDS—G_1–16. http://dx.doi.org/10.1527/tjsai.37-3_ids-g.

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50

Xu, Haotian, Haiyun Peng, Haoran Xie, Erik Cambria, Liuyang Zhou, and Weiguo Zheng. "End-to-End latent-variable task-oriented dialogue system with exact log-likelihood optimization." World Wide Web 23, no. 3 (2019): 1989–2002. http://dx.doi.org/10.1007/s11280-019-00688-8.

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