Дисертації з теми "Language processing tasks"
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Medlock, Benjamin William. "Investigating classification for natural language processing tasks." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611949.
Повний текст джерелаDyson, Lucy. "Insights into language processing in aphasia from semantic priming and semantic judgement tasks." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/19144/.
Повний текст джерелаZahidin, Ahmad Zamri. "Using Ada tasks (concurrent processing) to simulate a business system." Virtual Press, 1988. http://liblink.bsu.edu/uhtbin/catkey/539634.
Повний текст джерелаDepartment of Computer Science
Laws, Florian [Verfasser], and Hinrich [Akademischer Betreuer] Schütze. "Effective active learning for complex natural language processing tasks / Florian Laws. Betreuer: Hinrich Schütze." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2013. http://d-nb.info/1030521204/34.
Повний текст джерелаLorello, Luca Salvatore. "Small transformers for Bioinformatics tasks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23883/.
Повний текст джерелаCuriel, Diaz Arturo Tlacaélel. "Using formal logic to represent sign language phonetics in semi-automatic annotation tasks." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30308/document.
Повний текст джерелаThis thesis presents a formal framework for the representation of Signed Languages (SLs), the languages of Deaf communities, in semi-automatic recognition tasks. SLs are complex visio-gestural communication systems; by using corporal gestures, signers achieve the same level of expressivity held by sound-based languages like English or French. However, unlike these, SL morphemes correspond to complex sequences of highly specific body postures, interleaved with postural changes: during signing, signers use several parts of their body simultaneously in order to combinatorially build phonemes. This situation, paired with an extensive use of the three-dimensional space, make them difficult to represent with tools already existent in Natural Language Processing (NLP) of vocal languages. For this reason, the current work presents the development of a formal representation framework, intended to transform SL video repositories (corpus) into an intermediate representation layer, where automatic recognition algorithms can work under better conditions. The main idea is that corpora can be described with a specialized Labeled Transition System (LTS), which can then be annotated with logic formulae for its study. A multi-modal logic was chosen as the basis of the formal language: the Propositional Dynamic Logic (PDL). This logic was originally created to specify and prove properties on computer programs. In particular, PDL uses the modal operators [a] and to denote necessity and possibility, respectively. For SLs, a particular variant based on the original formalism was developed: the PDL for Sign Language (PDLSL). With the PDLSL, body articulators (like the hands or head) are interpreted as independent agents; each articulator has its own set of valid actions and propositions, and executes them without influence from the others. The simultaneous execution of different actions by several articulators yield distinct situations, which can be searched over an LTS with formulae, by using the semantic rules of the logic. Together, the use of PDLSL and the proposed specialized data structures could help curb some of the current problems in SL study; notably the heterogeneity of corpora and the lack of automatic annotation aids. On the same vein, this may not only increase the size of the available datasets, but even extend previous results to new corpora; the framework inserts an intermediate representation layer which can serve to model any corpus, regardless of its technical limitations. With this, annotations is possible by defining with formulae the characteristics to annotate. Afterwards, a formal verification algorithm may be able to find those features in corpora, as long as they are represented as consistent LTSs. Finally, the development of the formal framework led to the creation of a semi-automatic annotator based on the presented theoretical principles. Broadly, the system receives an untreated corpus video, converts it automatically into a valid LTS (by way of some predefined rules), and then verifies human-created PDLSL formulae over the LTS. The final product, is an automatically generated sub-lexical annotation, which can be later corrected by human annotators for their use in other areas such as linguistics
Milajevs, Dmitrijs. "A study of model parameters for scaling up word to sentence similarity tasks in distributional semantics." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/36225.
Повний текст джерелаAl-Hadlaq, Mohammed S. "Retention of words learned incidentally by Saudi EFL learners through working on vocabulary learning tasks constructed to activate varying depths of processing." Virtual Press, 2003. http://liblink.bsu.edu/uhtbin/catkey/1263891.
Повний текст джерелаDepartment of English
Chen, Charles L. "Neural Network Models for Tasks in Open-Domain and Closed-Domain Question Answering." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1578592581367428.
Повний текст джерелаMalapetsa, Christina. "Stroop tasks with visual and auditory stimuli : How different combinations of spoken words, written words, images and natural sounds affect reaction times." Thesis, Stockholms universitet, Institutionen för lingvistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-185057.
Повний текст джерелаElliot, Mark James. "A computational model of task oriented discourse." Thesis, University of Nottingham, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284055.
Повний текст джерелаFelce, Catherine. "L’ouverture de l’énoncé en allemand L2 : De la compréhension d’un phénomène à son appropriation et à son enseignement. Perspectives en didactique des langues." Thesis, Sorbonne Paris Cité, 2015. http://www.theses.fr/2015USPCA157/document.
Повний текст джерелаThis study concerns the acquisition of language specific discourse preferences in the first years of German as a foreign language in French secondary schools, and how new approaches could improve language instruction. Instead of focusing on verbal placement in declaratives, we decided to consider how learners start a sentence; that is, which constituent they decide to put before the finite verb form. The initial field in German (called pre-field) represents a syntactically undetermined position as it can be occupied by a variety of elements. To understand the constraints which influence the choice of the first constituent in a sentence, textual categories, as well as pragmatic and information-structural criteria, are required. These aspects were incorporated in the tasks the learners worked on in the classroom, in order to make them use such principles, and to modify the processing preferences they may have built up during acquisition of their L1. Acquisition draws on internal processes which set limits to instructional intervention. Practitioners should take these limitations into account if they aim to elaborate instructional proposals with linguistic and psycholinguistic relevance. Drawing on findings from the SLA research, we analyse the beginnings of sentences in a corpus of written and oral learner samples. We used these empirical observations as a guideline to redesign proposals for an instructional intervention which better fits a learning progression. Notions from different aspects of linguistics contribute to highlight specific functions of sentence beginnings in German. It would be possible to integrate these functions in a teaching programme. SLA research offers a theoretical framework to our research, as the findings provide a better understanding of the cognitive dimension of learning and the notions constitute a theoretical backing for our didactical proposals
Røkenes, Håkon Drolsum. "Graph-based Natural Language Processing : Graph edit distance applied to the task of detecting plagiarism." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-20778.
Повний текст джерелаUgalde-Gonzalez, Arturo. "Processing conditions as influences on task-based foreign language performance : a longitudinal study of communication strategies." Thesis, King's College London (University of London), 2006. https://kclpure.kcl.ac.uk/portal/en/theses/processing-conditions-as-influences-on-taskbased-foreign-language-performance--a-longitudinal-study-of-communication-strategies(5e7d3767-5b17-4a47-abb5-df068881a0f8).html.
Повний текст джерелаAlamry, Ali. "Grammatical Gender Processing in Standard Arabic as a First and a Second Language." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39965.
Повний текст джерелаSuddrey, Gavin. "Instructing and training robots through a natural language dialogue." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/229145/1/Gavin_Suddrey_Thesis.pdf.
Повний текст джерелаHikima, Noriko. "The effects of processing instruction and re-exposure on interpretation discourse level tasks : the case of Japanese passive forms." Thesis, University of Greenwich, 2010. http://gala.gre.ac.uk/6604/.
Повний текст джерелаAcosta, Andrew D. "Laff-O-Tron: Laugh Prediction in TED Talks." DigitalCommons@CalPoly, 2016. https://digitalcommons.calpoly.edu/theses/1667.
Повний текст джерелаZezinka, Alexandra. "Investigation of a Computerized Nonverbal Word-Picture Verification Task in Healthy Adults." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1491932143220066.
Повний текст джерелаFoster, Pauline Mary. "Attending to message and medium : the effects of planning time on the task-based language performance of native and non-native speakers." Thesis, King's College London (University of London), 2000. https://kclpure.kcl.ac.uk/portal/en/theses/attending-to-message-and-medium--the-effects-of-planning-time-on-the-taskbased-language-performance-of-native-and-nonnative-speakers(528f66b6-1324-4ca7-a76c-0f4b3247e14a).html.
Повний текст джерелаEnzinna, Naomi R. "The Processing of Preposition-Stranding Constructions in English." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/889.
Повний текст джерелаMasellis, Maria C. "Evidence for temporal processing deficits in children with attention deficit hyperactivity disorder and language impairments on a dichotic listening task." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0007/MQ40662.pdf.
Повний текст джерелаSerafini, Sandra. "Functional neuroanatomy during language processing : correspondence of cortical stimulation mapping, fMRI, PEPSI, and ERP during a visual object naming task /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/8235.
Повний текст джерелаSimonis, Rita. "The effects of multilingualism on executive processing." Thesis, Stockholms universitet, Centrum för tvåspråkighetsforskning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-157571.
Повний текст джерелаWidjaja, Hendra. "Visor++ : a software visualisation tool for task-parallel object-orientated programs." Title page, abstract and contents only, 1998. http://web4.library.adelaide.edu.au/theses/09AS/09asw639.pdf.
Повний текст джерелаGan, Gabriela, Christian Büchel, and Frédéric Isel. "Effect of language task demands on the neural response during lexical access: a functional magnetic resonance imaging study." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-127023.
Повний текст джерелаEyorokon, Vahid. "Measuring Goal Similarity Using Concept, Context and Task Features." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1534084289041091.
Повний текст джерелаTovedal, Sofiea. "On The Effectiveness of Multi-TaskLearningAn evaluation of Multi-Task Learning techniques in deep learning models." Thesis, Umeå universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172257.
Повний текст джерелаGibson, Bob. "Think aloud, non-continuous reporting, and annotated cloze : using verbal report and a self-coding procedure in looking at German and Japanese informants' processing of a second language cloze task." Thesis, University of Edinburgh, 2005. http://hdl.handle.net/1842/24611.
Повний текст джерелаDockes, Jérôme. "Statistical models for comprehensive meta-analysis of neuroimaging studies." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT048.
Повний текст джерелаThousands of neuroimaging studies are published every year. Exploiting this huge amount of results is difficult. Indeed, individual studies lack statistical power and report many spurious findings. Even genuine effects are often specific to particular experimental settings and difficult to reproduce. Meta- analysis aggregates studies to identify consistent trends in reported associations between brain structure and behavior. The standard approach to meta-analysis starts by gathering a sample of studies that investigate a same mental process or disease.Then, a statistical test delineates brain regions where there is a significant agreement among reported findings. In this thesis, we develop a different kind of metaanalysis that focuses on prediction rather than hypothesis testing. We build predictive models that map textual descriptions of experiments, mental processes or diseases to anatomical regions in the brain. Our supervised learning approach comes with a natural quantitative evaluation framework, and we conduct extensive experiments to validate and compare statistical models. We collect and share the largest existing dataset of neuroimaging studies and stereotactic coordinates. This dataset contains the full text and locations of neurological observations for over 13 000 publications. In the last part, we turn to decoding: inferring mental states from brain activity.We perform this task through meta-analysis of fMRI statistical maps collected from an online data repository. We use fMRI data to distinguish a wide range of mental conditions. Standard meta-analysis is an essential tool to distinguish true discoveries from noise and artifacts. This thesis introduces methods for predictive metaanalysis, which complement the standard approach and help interpret neuroimaging results and formulate hypotheses or formal statistical priors
Chan, Siu-Yuen. "Efficent user level infrastructure support for adaptive parallel computing on heterogenous networks of workstations." Thesis, Queensland University of Technology, 2000.
Знайти повний текст джерелаScarlato, Michele. "Sicurezza di rete, analisi del traffico e monitoraggio." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3223/.
Повний текст джерелаYi, Li. "Why Do Young Children Fail in False Belief Tasks: Linguistic Representations and Implicit Processing." Diss., 2009. http://hdl.handle.net/10161/1192.
Повний текст джерелаDespite recent evidence that infants under one year of age have implicit understanding of theory of mind, three-year-old children repeatedly fail in traditional false belief tasks. A serious of 4 studies investigated two possible sources of errors. First, children's comprehension of theory of mind questions was tested in an elicited imitation task. Second, their understanding of mental events was measured using anticipatory eye movements in non-verbal tasks. Results showed that young children's performance in verbal false belief tasks is limited by their understanding of linguistic representations of beliefs and their ability to monitor mental states in real-time. This implies the limitations of young children in keeping track of complex social events in real time and in understanding language conventions in real time.
Dissertation
Sturrock, Sara Katheleen. "Electroencephalographic correlates of cerebral engagement in auditory and visual language processing tasks in persons with down syndrome." Thesis, 2002. http://hdl.handle.net/2429/13260.
Повний текст джерелаHenriques, Daniel Filipe Rodrigues. "Automatic Completion of Text-based Tasks." Master's thesis, 2019. http://hdl.handle.net/10362/92296.
Повний текст джерелаNouri, Golmaei Sara. "Improving the Performance of Clinical Prediction Tasks by using Structured and Unstructured Data combined with a Patient Network." Thesis, 2021. http://dx.doi.org/10.7912/C2/41.
Повний текст джерелаWith the increasing availability of Electronic Health Records (EHRs) and advances in deep learning techniques, developing deep predictive models that use EHR data to solve healthcare problems has gained momentum in recent years. The majority of clinical predictive models benefit from structured data in EHR (e.g., lab measurements and medications). Still, learning clinical outcomes from all possible information sources is one of the main challenges when building predictive models. This work focuses mainly on two sources of information that have been underused by researchers; unstructured data (e.g., clinical notes) and a patient network. We propose a novel hybrid deep learning model, DeepNote-GNN, that integrates clinical notes information and patient network topological structure to improve 30-day hospital readmission prediction. DeepNote-GNN is a robust deep learning framework consisting of two modules: DeepNote and patient network. DeepNote extracts deep representations of clinical notes using a feature aggregation unit on top of a state-of-the-art Natural Language Processing (NLP) technique - BERT. By exploiting these deep representations, a patient network is built, and Graph Neural Network (GNN) is used to train the network for hospital readmission predictions. Performance evaluation on the MIMIC-III dataset demonstrates that DeepNote-GNN achieves superior results compared to the state-of-the-art baselines on the 30-day hospital readmission task. We extensively analyze the DeepNote-GNN model to illustrate the effectiveness and contribution of each component of it. The model analysis shows that patient network has a significant contribution to the overall performance, and DeepNote-GNN is robust and can consistently perform well on the 30-day readmission prediction task. To evaluate the generalization of DeepNote and patient network modules on new prediction tasks, we create a multimodal model and train it on structured and unstructured data of MIMIC-III dataset to predict patient mortality and Length of Stay (LOS). Our proposed multimodal model consists of four components: DeepNote, patient network, DeepTemporal, and score aggregation. While DeepNote keeps its functionality and extracts representations of clinical notes, we build a DeepTemporal module using a fully connected layer stacked on top of a one-layer Gated Recurrent Unit (GRU) to extract the deep representations of temporal signals. Independent to DeepTemporal, we extract feature vectors of temporal signals and use them to build a patient network. Finally, the DeepNote, DeepTemporal, and patient network scores are linearly aggregated to fit the multimodal model on downstream prediction tasks. Our results are very competitive to the baseline model. The multimodal model analysis reveals that unstructured text data better help to estimate predictions than temporal signals. Moreover, there is no limitation in applying a patient network on structured data. In comparison to other modules, the patient network makes a more significant contribution to prediction tasks. We believe that our efforts in this work have opened up a new study area that can be used to enhance the performance of clinical predictive models.
LIN, JI-WEI, and 林季緯. "Multi-label Classification with Multi-task learning - A case study of Natural Language Processing." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9a6q4q.
Повний текст джерела國立交通大學
工業工程與管理系所
108
Natural language processing (NLP) is always an important research topic. Many NLP problems such as spam filtering, text classification, movie rating, and toxic comment detection, are present in our daily life. In recent years, with the development of deep learning and the improvement of computing power, many studies have applied deep learning to deal with NLP problems. Deep learning model uses a large amount of training data to automatically extract features, giving a base to extract important and discriminative features from data, and achieve promising results. Therefore, deep learning has been widely used in speech recognition, object detection and image recognition, and has achieved good results. Multi-label classification problem refers to the problem that each data sample may have multiple labels. In the toxic comments classification problem, a comment may consists of both identity hates and threats, while the other comments may contain threats, obscene and toxic. This thesis proposes to use multi-task learning to deal with multi-label classification problems, in which we treat each single label as a single task. For each task, it is possible to improve the performance by sharing representation between tasks. This thesis uses three different NLP classification datasets to conduct experiments. We use our proposed multi-task learning architecture to deal with this problem. Since the natural language processing problem is a sequence problem, and the Recurrent Neural Networks (RNN) architecture is more capable of processing sequence problems in the deep learning model. Therefore, we construct our model through bidirectional GRU, and then apply CNN for further feature learning. This thesis conducts experiments on three datasets. The experimental results indicate that our proposed method outperforms the other alternatives.
"An in-depth look of phonological processing in Chinese character recognition: the effects of task." 1997. http://library.cuhk.edu.hk/record=b5889289.
Повний текст джерела"Domain-Agnostic Context-Aware Assistant Framework for Task-Based Environment." Master's thesis, 2020. http://hdl.handle.net/2286/R.I.57245.
Повний текст джерелаDissertation/Thesis
Masters Thesis Software Engineering 2020
Werfelmann, Robert. "A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task." Thesis, 2018. http://hdl.handle.net/10754/627954.
Повний текст джерелаGaray, Lucas Gonzalo. "Procesamiento de imágenes médicas para generación automática de reportes." Bachelor's thesis, 2019. http://hdl.handle.net/11086/13418.
Повний текст джерелаEn el presente trabajo se plantea el problema de la generación automática de reportes médicos a partir de imágenes. La redacción de informes que interpretan las imágenes médicas consume gran parte del tiempo de los especialistas. Además, en muchos casos se trata de una tarea muy repetitiva. En este contexto, un texto generado automáticamente puede reducir el trabajo del médico, que en lugar de redactar el texto completo se enfocará en revisar y modificar un texto generado automáticamente. El objetivo de esta tesis es consolidar una implementación basada en redes neuronales para descripción textual de imágenes. Para ello, se utilizará una arquitectura provista para la descripción de imágenes genéricas y se aplicará en este dominio médico. Finalmente se hará una comparación con otras implementaciones específicas de dominio y se compararán los resultados de forma cuantitativa y cualitativa. La principal dificultad que se presenta es la escasez de datos disponibles, porque a pesar de que se generan grandes volúmenes de datos, no siempre se encuentran disponibles para su uso. Para resolver este problema se aplicarán técnicas tales como subsampling y suprasampling. Otro problema detectado refiere a la métrica estándar de evaluación, BLEU, la cual no mide la semejanza entre dos textos de la forma que esperaríamos. Para solucionar esto, se plantea el uso de la similitud coseno. Finalmente, se reportará el impacto de los word embeddings y el mecanismo de atención.
In the present work we expose a system which aim is to automatically generate medical reports from medical images. The specialists spend a lot of time writing reports from images. Moreover, most of the cases this is a very repetitive task. In this context, an automatic generated draft could reduce the doctor’s workload, which will not write the whole report by himself, instead can review and modify the automatic generated draft. This thesis objective is to consolidate a neural network based implementation for image captioning. For this task, we will use a provided architecture for generic image captioning but will use it for medical domain. At the end, we will do a quantitative and qualitative comparison between our generic approach and some specific domain approaches. The main difficulty is the lack of available data, because despite of the huge amount of that is generated, not all of this data is available and with free use. To solve this problem we will apply some techniques such as subsampling and suprasampling. Another detected problem refers to the standard metric, BLEU, which doesn’t capture the similarity of two texts the way that we expected. To address this problem, we propose the cosine similarity. Finally, we report the impact the of specific domain word embeddings and the attention mechanism.
Jaime, Rodrigo. "Aprendizaje activo para la extracción de relaciones en textos." Bachelor's thesis, 2019. http://hdl.handle.net/11086/13415.
Повний текст джерелаA la hora de realizar un trabajo de aprendizaje automático, podemos encontrarnos con una fuente abundante de datos sin etiquetar y que su etiquetado manual sea un proceso costoso. Una técnica útil en estos casos es permitir que el algoritmo de aprendizaje consulte a un oráculo sobre las etiquetas de ciertos datos seleccionados que el algoritmo considere importantes. Este tipo de aprendizaje semi‐supervisado iterativo se llama aprendizaje activo. Utilizando IEPY, un framework de extracción de información orientado a extracción de relaciones, construiremos un sistema de extracción de relaciones con aprendizaje activo. Realizaremos una serie de experimentos sobre selección de instancias y etiquetado de features sobre un corpus basándonos en el trabajo de Settles 2011 con el objetivo de acelerar inicialmente el desempeño del modelo.
When working on some machine learning tasks, we may have to deal with large pool of unlabeled data and the labeling process to be manual and resource‐intensive. A useful technique in these cases is to allow the learning algorithm to query an oracle about the labels of certain data selected as important by the algorithm. This kind of iterative semi‐supervised learning is known as active learning. Using IEPY, an information extraction framework oriented towards relationship extraction, we will build a relationship extractor system with active learning. We will also perform a series of experiments about instance selection and feature labeling on a corpus, based on the work from Settles 2011 with the mission of accelerating the model's initial performance.
Atapattu, Mudiyanselage Thushari Dilhani Atapattu. "A computational model for task-adapted knowledge organisation: improving learning through concept maps extracted from lecture slides." Thesis, 2015. http://hdl.handle.net/2440/95127.
Повний текст джерелаThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2015
Sankar, Chinnadhurai. "Neural approaches to dialog modeling." Thesis, 2020. http://hdl.handle.net/1866/24802.
Повний текст джерелаThis thesis by article consists of four articles which contribute to the field of deep learning, specifically in understanding and learning neural approaches to dialog systems. The first article takes a step towards understanding if commonly used neural dialog architectures effectively capture the information present in the conversation history. Through a series of perturbation experiments on popular dialog datasets, wefindthatcommonly used neural dialog architectures like recurrent and transformer-based seq2seq models are rarely sensitive to most input context perturbations such as missing or reordering utterances, shuffling words, etc. The second article introduces a simple and cost-effective way to collect large scale datasets for modeling task-oriented dialog systems. This approach avoids the requirement of a com-plex argument annotation schema. The initial release of the dataset includes 13,215 task-based dialogs comprising six domains and around 8k unique named entities, almost 8 times more than the popular MultiWOZ dataset. The third article proposes to improve response generation quality in open domain dialog systems by jointly modeling the utterances with the dialog attributes of each utterance. Dialog attributes of an utterance refer to discrete features or aspects associated with an utterance like dialog-acts, sentiment, emotion, speaker identity, speaker personality, etc. The final article introduces an embedding-free method to compute word representations on-the-fly. This approach significantly reduces the memory footprint which facilitates de-ployment in on-device (memory constraints) devices. Apart from being independent of the vocabulary size, we find this approach to be inherently resilient to common misspellings.