Spoken language understanding ( SLU ) is a key component of task-oriented dialogue systems . SLU parses natural language user utterances into semantic frames . We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems . A unified neural network framework is proposed to enable the system to first learn by supervision from a set of dialogue data and then continuously improve its behaviour via reinforcement learning , all using gradient-based algorithms on one single model . We describe a framework for deep linguistic processing for natural language understanding in task-oriented spoken dialogue systems . The goal is to create domaingeneral processing techniques that can be shared across all domains and dialogue tasks , combined with domain-specific optimization based on an ontology mapping from the generic LF to the application ontology . To design a task-independent dialogue system , we present a task-oriented dialogue analysis in terms of finding the referents of definite descriptions and we show how this analysis leads to a goal-oriented inferential representation of the task . This representation provides a logical generic model of the task , which is compatible with a belief system . Spoken and written interactions were compared using a real natural language information retrieval system . In two experiments , participants performed several task-oriented dialogue scenarios either with a phone or a Web interface . The goal was to locate restaurants with specific characteristics among these present in a database . Spoken and written interactions with a natural language dialogue system were compared using two real information-retrieval systems . In order to look for a restaurant ( Experiment 1 ) or plan a trip ( Experiment 2 ) , participants performed several task-oriented dialogue scenarios . Although the spoken interaction mode was less efficient , it promoted collaboration , the use of personal pronouns and the literal form of the system 's command utterances . The rapid development of speech processing and natural language processing ( NLP ) is leading to a higher demand on human-computer interaction based on the natural language . We do some research in the field of task-orientated-dialogue processing , especially in initiative and task analysis . The dialogue system developed is to guide visitors in a park . The view that communication is a type of action has provided a basis for research in natural language processing for many years . However , models of task-oriented dialogic interaction fail to fully exploit the language-as-action perspective because there is no deep understanding of the interaction between the definition of the task , the autonomy of the agents doing the task , and efficient language behavior . In this paper , I examine the interaction of three factors:(1 ) agents ’ autonomy ; ( 2 ) agents ’ resource bounds ; and ( 3 ) the distribution task-relevant information . For three years we have been developing a voice dialogue system capable of continuous speech recognition , natural language understanding , task-oriented dialogue managing . First , the architecture of the system is surveyed . This work investigates the task-oriented dialogue problem in mixed-domain settings . We study the effect of alternating between different domains in sequences of dialogue turns using two related state-of-the-art dialogue systems . Dialog state tracking in a spoken dialog system is the task that tracks the flow of a dialog and identifies accurately what a user wants from the utterance . Since the success of a dialog is influenced by the ability of the system to catch the requirements of the user , accurate state tracking is important for spoken dialog systems . As a result , the error propagation within a dialog system is minimized . To prove the effectiveness of the proposed model , we do experiments on dialog state tracking in the human-human task-oriented dialogs . Our experimental results show that the proposed method outperforms both the neural tracker without the attention mechanism and that without the hierarchical softmax . We show that an end-to-end dialog system based on Memory Networks can reach promising , yet imperfect , performance and learn to perform non-trivial operations . We confirm those results by comparing our system to a hand-crafted slot-filling baseline on data from the second Dialog State Tracking Challenge ( Henderson et al. , 2014a ) . We show similar result patterns on data extracted from an online concierge service . We also adopted a positive-negative model to abstract and generalize the effect of given user input and corresponding system output on dialog-state updating . Experiment result implies that our model can be used for dialog tracking without significant loss of performance . We implemented dialog system to verify that our approach is feasible in a practical Korean dialog system that can be adopted for other languages . Dialogue state tracking ( DST ) , when formulated as a supervised learning problem , relies on labelled data . Since dialogue state annotation usually requires labelling all turns of a single dialogue and utilizing context information , it is very expensive to annotate all available unlabelled data . In this paper , a novel cost-sensitive active learning framework is proposed based on a set of new dialogue-level query strategies . This paper addresses a user model and dialogue state definition in spoken consulting dialogue systems that help users in making decision . When selecting from a set of alternatives , users have various decision criteria for making decision . Experiments demonstrate that the proposed HKA can incorporate more appropriate knowledge and make the state-of-the-art models generate more informative responses . Further analysis shows that our HKA can improve the model ’s ability of dialogue state management , especially when the number of dialogue turns is large . Visual Dialog is a multimodal task of answering a sequence of questions grounded in an image , using the conversation history as context . It entails challenges in vision , language , reasoning , and grounding . Developing a robust emotion speech recognition ( SER ) system for human dialog is important in advancing conversational agent design . In this paper , we proposed a novel inference algorithm , a dialogical emotion decoding ( DED ) algorithm , that treats a dialog as a sequence and consecutively decode the emotion states of each utterance over time with a given recognition engine . 2 . The granularity of interaction is the sentence ; but for human dialog , interaction happens more frequently , in real time , often with overlapping utterances ; Given that such responsiveness is important to human language use , the question arises : how do we build systems with these abilities ? The obvious approach is to add these abilities to a meaning based speech system . Namely , the system is provided with a link protocol consisting of a set of messages , which is used by the link service in order to mutually interrelate information existing in one or more applications , and these open protocols make this linking system easy to carry out the non-cooperate development of application programs for use in this linking system . With this , the linking system for use in a computer environment which supports massaging and interactive dialog between application programs . COPYRIGHT : (C)1990,JPO Yet organizations are deploying AI algorithms in many mission-critical settings . In order to trust their behavior , we must make it intelligible --- either by using inherently interpretable models or by developing methods for explaining otherwise overwhelmingly complex decisions by local approximation , vocabulary alignment , and interactive dialog . Video dialog is a new and challenging task , which requires the agent to answer questions combining video information with dialog history . And different from single-turn video question answering , the additional dialog history is important for video dialog , which often includes contextual information for the question . Existing visual dialog methods mainly use RNN to encode the dialog history as a single vector representation , which might be rough and straightforward . Some more advanced methods utilize hierarchical structure , attention and memory mechanisms , which still lack an explicit reasoning process . In this paper , we introduce a novel progressive inference mechanism for video dialog , which progressively updates query information based on dialog history and video content until the agent think the information is sufficient and unambiguous . In order to tackle the multimodal fusion problem , we propose a crosstransformer module , which could learn more fine-grained and comprehensive interactions both inside and between the modalities . Such training allows the robot to reason not only about the learned concepts , but the spaces in-between them . We show a sample interaction dialog in which a user interactively grounds antonym adjective pairs with the robot , and data showing the state of the trained model . This research examined the response selection method of an interview-based dialog system that obtains the user ’s information by the chat-like conversation . In the interview dialog , the system should ask about the subject that the user is interested in to obtain the user ’s information efficiently . In this paper , we proposed the method to select the system ’s utterance based on the user ’s emotion to a focus detected from the user ’s utterance . In order to determine position of the user and his speech queries , the robot uses a set of web-cameras , microphone array and technologies for distant speech recognition and face tracking . There are some methods for interaction between users and computers , such as manual entry , verbal dialog , visual and emotional communications . Interaction model of the proposed mobile informational robot has 3 main parts : beginning of the dialogue , task execution and completion of the dialogue . visual grounding ) estimated from an image and question pair . We propose a novel attention mechanism that exploits visual attentions in the past to resolve the current reference in the visual dialog scenario . The proposed model is equipped with an associative attention memory storing a sequence of previous ( attention , key ) pairs . Visual dialog ( VisDial ) is a task which requires an AI agent to answer a series of questions grounded in an image . Unlike in visual question answering ( VQA ) , the series of questions should be able to capture a temporal context from a dialog history and exploit visually-grounded information . * 2 . Visualizing Dialogs * 3 . The Role of Tools in Development * 4 . section : Related Work Grounding language through vision has recently become the focus of several tasks , including Image Captioning ( IC , e.g. ) and Visual Question Answering ( VQA , eg . ) , and even more recently , Visual Reasoning and Visual Dialog . Our focus is on Grounded Textual Entailment ( GTE ) . For instance Park - Rhodes in Figure 1 is not in W. However , Park - Rhodes will be included in Q as name 2 ( since it is the second token of the name field ) which allows our model to generate it . This mechanism is inspired by recent work on attention based word copying for neural machine translation [ reference ] as well as delexicalization for neural dialog systems [ reference ] . Note that we only show some of recent sentences here , even the dialog has more sentences . Visualization of Dialog . Figure 5 shows visualization of models for dialog tasks . This system will accept as input a description of agents ' interactions with a task domain expressed in a fully-expressive belief logic with non-monotonic and temporal extensions . It generates an operational belief model for use in that domain , sufficient for the requirements of cooperative dialogue , including the negotiation of complex domain plans . The compiled model incorporates the belief simplification mentioned above , and also uses a simplified temporal logic of belief based on the restricted circumstances under which beliefs can change . User modelling is becoming recognized as a key issue in the design of cooperative dialogue systems . Assumptions about the beliefs , goals and plans of the user are seen as necessary prerequisites for a system to be capable of exhibiting user-oriented dialogue behaviour . Human-robot interaction is an important component for robots operating in human environments and verbal interaction is in many cases the most intuitive and effective solution for humans . Managing dialogues between physical agents interacting in a physical environment brings additional challenges to virtual dialogue systems ( eg . Siri or Google Now ) . We present and evaluate a novel approach to natural language generation ( NLG ) in statistical spoken dialogue systems ( SDS ) using a data-driven statistical optimization framework for incremental information presentation ( IP ) , where there is a trade-off to be solved between presenting “ enough " information to the user while keeping the utterances short and understandable . The trained IP model is adaptive to variation from the current generation context ( e.g. a user and a non-deterministic sentence planner ) , and it incrementally adapts the IP policy at the turn level . Compared to conventional hand-crafted rule-based dialogue management systems , statistical POMDP-based dialogue managers offer the promise of increased robustness , reduced development and maintenance costs , and scaleability to large open-domains . As a consequence , there has been considerable research activity in approaches to statistical spoken dialogue systems over recent years . However , building and deploying a real-time spoken dialogue system is expensive , and even when operational , it is hard to recruit sufficient users to get statistically significant results . End-to-end models for strategic dialogue are challenging to train , because linguistic and strategic aspects are entangled in latent state vectors . We introduce an approach to generating latent representations of dialogue moves , by inducing sentence representations to maximize the likelihood of subsequent sentences and actions . Resume . The multiplication of trades , management and decision levels in semiautonomous regions generates communication difficulties for internal management dialogue , between humans as well as between ERP systems . Yet human interoperability , just like machine interoperability is essential in managing the organisation and providing the information that decision makers need to clarify . As a result , future computer professionals who are familiar with the Slow Tech approach will be able to collaborate much more easily across the kind of cross disciplinary teams suited to design human centred , sustainable and desirable technologies . , – Rather than simply focusing on the role of computer professionals , all members of society are called to play a new role in the design of future ICT scenarios . Starting a societal dialogue that involves computer professionals , users , researchers , designers , ICT industrialists , and policy makers is very much needed . , – The value of this paper is in its call for reflection followed by action . Based on an holistic approach to the design of new ICT systems , the paper advocates a new starting point for systems design : it should be based on a long-term view of the desirability and social importance of technologies , their environmental impact and sustainability , and the fairness and equity of the conditions of workers involved in the computing manufacturing processes . Evaluating Visual Dialogue , the task of answering a sequence of questions relating to a visual input , remains an open research challenge . The current evaluation scheme of the VisDial dataset computes the ranks of ground-truth answers in predefined candidate sets , which Massiceti et al. ( 2018 ) show can be susceptible to the exploitation of dataset biases . Three distinct content management systems are modeled and compared in order to identify how these methods conceptually apply to achieve specific benefits . Our early progress promises worthwhile improvements in user experiences of personal content , and the opportunity to crossover this approach into other topics , such as gaming and group dialogue . Based on the learner model the system plans the appropriate --personalized for learners-- reflective tutorial dialogue in order to promote their reflection , a fact which leads them towards scientific thought . The system consists of two parts : ( 1 ) the Diagnosis part and ( 2 ) the Reflective Tutorial Dialogue part . In this paper we present the dialogue strategies , tactics and plans which are used by the dialogue part for the generation of the appropriate for learners ’ reflective learning dialogues according to their learner models . In this paper we explore the utility of the Navigation Map ( NM ) , a graphical representation of the discourse structure . We run a user study to investigate if users perceive the NM as helpful in a tutoring spoken dialogue system . From the users ’ perspective , our results show that the NM presence allows them to better identify and follow the tutoring plan and to better integrate the instruction .
Spoken language understanding ( SLU ) is a key component of task-oriented dialogue systems . SLU parses natural language user utterances into semantic frames .
We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems . A unified neural network framework is proposed to enable the system to first learn by supervision from a set of dialogue data and then continuously improve its behaviour via reinforcement learning , all using gradient-based algorithms on one single model .
We describe a framework for deep linguistic processing for natural language understanding in task-oriented spoken dialogue systems . The goal is to create domaingeneral processing techniques that can be shared across all domains and dialogue tasks , combined with domain-specific optimization based on an ontology mapping from the generic LF to the application ontology .
To design a task-independent dialogue system , we present a task-oriented dialogue analysis in terms of finding the referents of definite descriptions and we show how this analysis leads to a goal-oriented inferential representation of the task . This representation provides a logical generic model of the task , which is compatible with a belief system .
Spoken and written interactions were compared using a real natural language information retrieval system . In two experiments , participants performed several task-oriented dialogue scenarios either with a phone or a Web interface . The goal was to locate restaurants with specific characteristics among these present in a database .
Spoken and written interactions with a natural language dialogue system were compared using two real information-retrieval systems . In order to look for a restaurant ( Experiment 1 ) or plan a trip ( Experiment 2 ) , participants performed several task-oriented dialogue scenarios . Although the spoken interaction mode was less efficient , it promoted collaboration , the use of personal pronouns and the literal form of the system 's command utterances .
The rapid development of speech processing and natural language processing ( NLP ) is leading to a higher demand on human-computer interaction based on the natural language . We do some research in the field of task-orientated-dialogue processing , especially in initiative and task analysis . The dialogue system developed is to guide visitors in a park .
The view that communication is a type of action has provided a basis for research in natural language processing for many years . However , models of task-oriented dialogic interaction fail to fully exploit the language-as-action perspective because there is no deep understanding of the interaction between the definition of the task , the autonomy of the agents doing the task , and efficient language behavior . In this paper , I examine the interaction of three factors:(1 ) agents ’ autonomy ; ( 2 ) agents ’ resource bounds ; and ( 3 ) the distribution task-relevant information .
For three years we have been developing a voice dialogue system capable of continuous speech recognition , natural language understanding , task-oriented dialogue managing . First , the architecture of the system is surveyed .
This work investigates the task-oriented dialogue problem in mixed-domain settings . We study the effect of alternating between different domains in sequences of dialogue turns using two related state-of-the-art dialogue systems .
Dialog state tracking in a spoken dialog system is the task that tracks the flow of a dialog and identifies accurately what a user wants from the utterance . Since the success of a dialog is influenced by the ability of the system to catch the requirements of the user , accurate state tracking is important for spoken dialog systems .
As a result , the error propagation within a dialog system is minimized . To prove the effectiveness of the proposed model , we do experiments on dialog state tracking in the human-human task-oriented dialogs . Our experimental results show that the proposed method outperforms both the neural tracker without the attention mechanism and that without the hierarchical softmax .
We show that an end-to-end dialog system based on Memory Networks can reach promising , yet imperfect , performance and learn to perform non-trivial operations . We confirm those results by comparing our system to a hand-crafted slot-filling baseline on data from the second Dialog State Tracking Challenge ( Henderson et al. , 2014a ) . We show similar result patterns on data extracted from an online concierge service .
We also adopted a positive-negative model to abstract and generalize the effect of given user input and corresponding system output on dialog-state updating . Experiment result implies that our model can be used for dialog tracking without significant loss of performance . We implemented dialog system to verify that our approach is feasible in a practical Korean dialog system that can be adopted for other languages .
Dialogue state tracking ( DST ) , when formulated as a supervised learning problem , relies on labelled data . Since dialogue state annotation usually requires labelling all turns of a single dialogue and utilizing context information , it is very expensive to annotate all available unlabelled data . In this paper , a novel cost-sensitive active learning framework is proposed based on a set of new dialogue-level query strategies .
This paper addresses a user model and dialogue state definition in spoken consulting dialogue systems that help users in making decision . When selecting from a set of alternatives , users have various decision criteria for making decision .
Experiments demonstrate that the proposed HKA can incorporate more appropriate knowledge and make the state-of-the-art models generate more informative responses . Further analysis shows that our HKA can improve the model ’s ability of dialogue state management , especially when the number of dialogue turns is large .
Visual Dialog is a multimodal task of answering a sequence of questions grounded in an image , using the conversation history as context . It entails challenges in vision , language , reasoning , and grounding .
Developing a robust emotion speech recognition ( SER ) system for human dialog is important in advancing conversational agent design . In this paper , we proposed a novel inference algorithm , a dialogical emotion decoding ( DED ) algorithm , that treats a dialog as a sequence and consecutively decode the emotion states of each utterance over time with a given recognition engine .
2 . The granularity of interaction is the sentence ; but for human dialog , interaction happens more frequently , in real time , often with overlapping utterances ; Given that such responsiveness is important to human language use , the question arises : how do we build systems with these abilities ? The obvious approach is to add these abilities to a meaning based speech system .
Namely , the system is provided with a link protocol consisting of a set of messages , which is used by the link service in order to mutually interrelate information existing in one or more applications , and these open protocols make this linking system easy to carry out the non-cooperate development of application programs for use in this linking system . With this , the linking system for use in a computer environment which supports massaging and interactive dialog between application programs . COPYRIGHT : (C)1990,JPO
Yet organizations are deploying AI algorithms in many mission-critical settings . In order to trust their behavior , we must make it intelligible --- either by using inherently interpretable models or by developing methods for explaining otherwise overwhelmingly complex decisions by local approximation , vocabulary alignment , and interactive dialog .
Video dialog is a new and challenging task , which requires the agent to answer questions combining video information with dialog history . And different from single-turn video question answering , the additional dialog history is important for video dialog , which often includes contextual information for the question . Existing visual dialog methods mainly use RNN to encode the dialog history as a single vector representation , which might be rough and straightforward . Some more advanced methods utilize hierarchical structure , attention and memory mechanisms , which still lack an explicit reasoning process . In this paper , we introduce a novel progressive inference mechanism for video dialog , which progressively updates query information based on dialog history and video content until the agent think the information is sufficient and unambiguous . In order to tackle the multimodal fusion problem , we propose a crosstransformer module , which could learn more fine-grained and comprehensive interactions both inside and between the modalities .
Such training allows the robot to reason not only about the learned concepts , but the spaces in-between them . We show a sample interaction dialog in which a user interactively grounds antonym adjective pairs with the robot , and data showing the state of the trained model .
This research examined the response selection method of an interview-based dialog system that obtains the user ’s information by the chat-like conversation . In the interview dialog , the system should ask about the subject that the user is interested in to obtain the user ’s information efficiently . In this paper , we proposed the method to select the system ’s utterance based on the user ’s emotion to a focus detected from the user ’s utterance .
In order to determine position of the user and his speech queries , the robot uses a set of web-cameras , microphone array and technologies for distant speech recognition and face tracking . There are some methods for interaction between users and computers , such as manual entry , verbal dialog , visual and emotional communications . Interaction model of the proposed mobile informational robot has 3 main parts : beginning of the dialogue , task execution and completion of the dialogue .
visual grounding ) estimated from an image and question pair . We propose a novel attention mechanism that exploits visual attentions in the past to resolve the current reference in the visual dialog scenario . The proposed model is equipped with an associative attention memory storing a sequence of previous ( attention , key ) pairs .
Visual dialog ( VisDial ) is a task which requires an AI agent to answer a series of questions grounded in an image . Unlike in visual question answering ( VQA ) , the series of questions should be able to capture a temporal context from a dialog history and exploit visually-grounded information .
* 2 . Visualizing Dialogs * 3 . The Role of Tools in Development * 4 .
section : Related Work Grounding language through vision has recently become the focus of several tasks , including Image Captioning ( IC , e.g. ) and Visual Question Answering ( VQA , eg . ) , and even more recently , Visual Reasoning and Visual Dialog . Our focus is on Grounded Textual Entailment ( GTE ) .
For instance Park - Rhodes in Figure 1 is not in W. However , Park - Rhodes will be included in Q as name 2 ( since it is the second token of the name field ) which allows our model to generate it . This mechanism is inspired by recent work on attention based word copying for neural machine translation [ reference ] as well as delexicalization for neural dialog systems [ reference ] .
Note that we only show some of recent sentences here , even the dialog has more sentences . Visualization of Dialog . Figure 5 shows visualization of models for dialog tasks .
This system will accept as input a description of agents ' interactions with a task domain expressed in a fully-expressive belief logic with non-monotonic and temporal extensions . It generates an operational belief model for use in that domain , sufficient for the requirements of cooperative dialogue , including the negotiation of complex domain plans . The compiled model incorporates the belief simplification mentioned above , and also uses a simplified temporal logic of belief based on the restricted circumstances under which beliefs can change .
User modelling is becoming recognized as a key issue in the design of cooperative dialogue systems . Assumptions about the beliefs , goals and plans of the user are seen as necessary prerequisites for a system to be capable of exhibiting user-oriented dialogue behaviour .
Human-robot interaction is an important component for robots operating in human environments and verbal interaction is in many cases the most intuitive and effective solution for humans . Managing dialogues between physical agents interacting in a physical environment brings additional challenges to virtual dialogue systems ( eg . Siri or Google Now ) .
We present and evaluate a novel approach to natural language generation ( NLG ) in statistical spoken dialogue systems ( SDS ) using a data-driven statistical optimization framework for incremental information presentation ( IP ) , where there is a trade-off to be solved between presenting “ enough " information to the user while keeping the utterances short and understandable . The trained IP model is adaptive to variation from the current generation context ( e.g. a user and a non-deterministic sentence planner ) , and it incrementally adapts the IP policy at the turn level .
Compared to conventional hand-crafted rule-based dialogue management systems , statistical POMDP-based dialogue managers offer the promise of increased robustness , reduced development and maintenance costs , and scaleability to large open-domains . As a consequence , there has been considerable research activity in approaches to statistical spoken dialogue systems over recent years . However , building and deploying a real-time spoken dialogue system is expensive , and even when operational , it is hard to recruit sufficient users to get statistically significant results .
End-to-end models for strategic dialogue are challenging to train , because linguistic and strategic aspects are entangled in latent state vectors . We introduce an approach to generating latent representations of dialogue moves , by inducing sentence representations to maximize the likelihood of subsequent sentences and actions .
Resume . The multiplication of trades , management and decision levels in semiautonomous regions generates communication difficulties for internal management dialogue , between humans as well as between ERP systems . Yet human interoperability , just like machine interoperability is essential in managing the organisation and providing the information that decision makers need to clarify .
As a result , future computer professionals who are familiar with the Slow Tech approach will be able to collaborate much more easily across the kind of cross disciplinary teams suited to design human centred , sustainable and desirable technologies . , – Rather than simply focusing on the role of computer professionals , all members of society are called to play a new role in the design of future ICT scenarios . Starting a societal dialogue that involves computer professionals , users , researchers , designers , ICT industrialists , and policy makers is very much needed . , – The value of this paper is in its call for reflection followed by action . Based on an holistic approach to the design of new ICT systems , the paper advocates a new starting point for systems design : it should be based on a long-term view of the desirability and social importance of technologies , their environmental impact and sustainability , and the fairness and equity of the conditions of workers involved in the computing manufacturing processes .
Evaluating Visual Dialogue , the task of answering a sequence of questions relating to a visual input , remains an open research challenge . The current evaluation scheme of the VisDial dataset computes the ranks of ground-truth answers in predefined candidate sets , which Massiceti et al. ( 2018 ) show can be susceptible to the exploitation of dataset biases .
Three distinct content management systems are modeled and compared in order to identify how these methods conceptually apply to achieve specific benefits . Our early progress promises worthwhile improvements in user experiences of personal content , and the opportunity to crossover this approach into other topics , such as gaming and group dialogue .
Based on the learner model the system plans the appropriate --personalized for learners-- reflective tutorial dialogue in order to promote their reflection , a fact which leads them towards scientific thought . The system consists of two parts : ( 1 ) the Diagnosis part and ( 2 ) the Reflective Tutorial Dialogue part . In this paper we present the dialogue strategies , tactics and plans which are used by the dialogue part for the generation of the appropriate for learners ’ reflective learning dialogues according to their learner models .
In this paper we explore the utility of the Navigation Map ( NM ) , a graphical representation of the discourse structure . We run a user study to investigate if users perceive the NM as helpful in a tutoring spoken dialogue system . From the users ’ perspective , our results show that the NM presence allows them to better identify and follow the tutoring plan and to better integrate the instruction .
Transfer Learning for Context-Aware Spoken Language Understanding Continuously Learning Neural Dialogue Management Deep Linguistic Processing for Spoken Dialogue Systems Logical Task Modelling for Man-Machine Dialogue Ordre des informations et effet de modalité pour une recherche de restaurants Effect of modality on collaboration with a dialogue system Initiative and task analyzing in the dialogue processing Efficiency Tradeoffs for Language and Action in Collaborative Tasks Task-oriented dialogue processing in human-computer voice communication Towards Task-Oriented Dialogue in Mixed Domains A Two-Step Neural Dialog State Tracker for Task-Oriented Dialog Processing An End-to-End Neural Dialog State Tracking for Task-Oriented Dialogs Learning End-to-End Goal-Oriented Dialog Implementation of generic positive-negative tracker in extensible dialog system Cost-Sensitive Active Learning for Dialogue State Tracking Dialogue strategy optimization to assist user's decision for spoken consulting dialogue systems HKA: A Hierarchical Knowledge Attention Mechanism for Multi-Turn Dialogue System CLEVR-Dialog: A Diagnostic Dataset for Multi-Round Reasoning in Visual Dialog A Dialogical Emotion Decoder for Speech Motion Recognition in Spoken Dialog A Responsive Dialog System Method and apparatus for linking service Intelligible Artificial Intelligence Video Dialog via Progressive Inference and Cross-Transformer Grounding Antonym Adjective Pairs through Interaction Response Selection of Interview-Based Dialog System Using User Focus and Semantic Orientation Multimodal human-robot interaction Visual Reference Resolution using Attention Memory for Visual Dialog Dual Attention Networks for Visual Reference Resolution in Visual Dialog Practical Spoken Dialog Systems Grounded Textual Entailment Neural Text Generation from Structured Data with Application to the Biography Domain Query-Reduction Networks for Question Answering Generating efficient belief models for task-oriented dialogues VIE-DPM: A User Model in a Natural-Language Dialog System Architecture and representation for handling dialogues in human-robot interactions Natural Language Generation as Incremental Planning Under Uncertainty: Adaptive Information Presentation for Statistical Dialogue Systems Evaluation of Statistical POMDP-Based Dialogue Systems in Noisy Environments Hierarchical Text Generation and Planning for Strategic Dialogue Introduction de l'ingénierie ontologique dans la méthodologie de développement des progiciels de gestion des collectivités territoriales Slow Tech: a quest for good, clean and fair ICT A Revised Generative Evaluation of Visual Dialogue Improving the Value of Archived Personal Content with Aesthetic and Reflexive Qualities Historical Text Comprehension Reflective Tutorial Dialogue System The Utility of a Graphical Representation of Discourse Structure in Spoken Dialogue Systems
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ASRU AAAI IHM FUZZ-IEEE ASRU SIGDIAL Conference SLT ICASSP NAACL-HLT (1) ICASSP EMNLP/IJCNLP (1) ICUMT COLING EMNLP ICLR GWAI APSIPA EGC CCNC ACL
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