Since the voice fundamental frequency ( F 0 ) contour is an important acoustic correlate of many prosodic constructs , modeling and analyzing F 0 contours can potentially be useful for many speech applications such as speech synthesis , speaker identification , speech conversion , and dialogue systems , in which prosodic information plays a significant role . In this chapter , we formulate a statistical model of F 0 contours by translating the “ Fujisaki model , ” a well-founded mathematical model representing the control mechanism of vocal fold vibration , into a probabilistic model described as a discrete-time stochastic process . Analysis of speech acts and discourse structures is essential to a dialogue understanding system because speech acts and discourse structures are closely tied with the speaker ’s intention . However , it has been difficult to infer a speech act and a discourse structure from a surface utterance because they highly depend on the context of the utterance . Representing large lexical data bases , treating sophisticated phonological and morphological transformations , and processing in real time large corpuses demands fast finite-state methods toolkits . Analysing the syntactic structure , computing anaphoric relations , and dealing with the representation of information flow in dialogue understanding , demands the processing of complex constraints on graph structures , with sophisticated sharing of large non-deterministic search spaces . In customer service ( CS ) , customers pose questions that will be answered by customer service staff , and the communication in CS is a typical multi-round conversation . However , there are no explicit correspondences among conversational utterances , and obtaining the explicit alignments of those utterances not only contributes to dialogue analysis but also provides valuable data for learning intelligent dialogue systems . In this paper , we first present a study on utterance alignment ( UA ) in CS . The importance of the Internet grows dramatically from year to year in more or less all disciplines . This volume contains the papers ( 12 contributions in English and 6 in German ) read at an international meeting of the IADA ( International Association for Dialogue Analysis , Bologna ) in Erlangen , April 2 3 , 1998 . There were two sections at the conference , one dealt with the new means of electronic communication , mainly with E-Mail communication and with Internet Relay Chat ( IRC ) , the other , more traditional , dealt with dialogues on television and the radio , or with specific aspects of Mass Media communication . " The article begins by explaining how the dialogues were created and then discusses the benefits and potential shortcomings of this approach in the context of interpreter education . The main points of discussion concern ( 1 ) the level of systematicity and authenticity that can be achieved with this corpus-based approach ; and ( 2 ) the potential of a 3D virtual environment to increase this sense of authenticity and thus to enable students to experience the essence of dialogue interpreting in a simulated environment . Anchoring dialogue interpreting in principles of teaching and learning . In today ’s multilingual societies cross-linguistic communication is increasingly frequent , especially when it relates to accessing services . It is shown that , when communicative functions are assigned to functional segments , thereby eliminating every form of segmentation-related multifunctionality , an average multifunctionality is found between 1.8 and 3.6 , depending on what is considered to count as a segment 's communicative function . Moreover , a good understanding of the nature of the relations among the various multiple functions that a segment may have , and of the relations between functional segments and other units in dialogue segmentation , opens the way for defining a multidimensional computational update semantics for dialogue interpretation . Dialogue Act ( DA ) classification is a challenging problem in dialogue interpretation , which aims to attach semantic labels to utterances and characterize the speaker ’s intention . Currently , many existing approaches formulate the DA classification problem ranging from multi-classification to structured prediction , which suffer from two limitations : a ) these methods are either handcrafted feature-based or have limited memories . The features of OntoVPA are offered as generic solutions to core DM problems , such as dialogue state tracking , anaphora and coreference resolution , etc . To the best of our knowledge , OntoVPA is the first commercially available , fully implemented DMS that employs ontologies and ontology-based rules for ( a ) domain model representation and reasoning , ( b ) dialogue representation and state tracking , and ( c ) response generation . OntoVPA is a declarative , knowledge-based system which can be customized to a new VPA domain by modifying and exchanging ontologies and rule bases , with very little to no conventional programming required . The advent of VoiceXML for voice applications contributed to reduce the proliferation of incompatible dialogue interpreters , but introduced new complexity . As a response to these issues , we designed an architecture for dialogue representation and interpretation , AdaRTE , which allows developers to layout dialogue interactions through a high level formalism that offers both declarative and procedural features . AdaRTE aim is to provide a ground for deploying complex and adaptable dialogues whilst allows the experimentation and incremental adoption of innovative speech technologies . The advent of VoiceXML reduced the proliferation of incompatible dialog formalisms , at the expense of adding even more complexity . This paper introduces a novel architecture for dialogue representation and interpretation , AdaRTE , which allows developers to lay out dialog interactions through a high-level formalism , offering both declarative and procedural features . AdaRTE 's aim is to provide a ground for deploying complex and adaptable dialogs whilst allowing experimentation and incremental adoption of innovative speech technologies . The aim of the project was to improve the quality of synthesised speech output to be used in dialogue systems as an additional element of multimodal man-machine interfaces . German text and dialogue interaction analysis ( theoretical research ) has been carried out to predict the tone groups ( TGs ) , the phrase boundaries in sentences and the place of the focus in the phrase . Tone groups represent the general intonation structure of the phrase not taking into account word level intonation . The resulting logic corresponds closely to the infinitevalued logic Łα , of Łukasiewicz . In fact , the approach provides a dialogue interpretation of Ł∞ and leads to a convenient method for establishing logical identities . Set theory is then developed , not by taking set as a primitive concept but by assuming each set A is determined by a property P characteristic of its members : A = {x : P(x)}. When this is expressed formally the result can be read in two ways according to whether the underlying logic is classical logic or Ł∞ ( with the above interpretation ) . With the implementation of authentic assessment toward the students who participate in the drama appreciation course . The obtained results show significant increase in their competence through the 6 kinds of task which contain website based authentic assessment on the stage of manipulating expression , scripting , dialogue reading , pre performing and stage performance . In this work we have investigated existing knowledge distillation methods for NMT ( which work at the word - level ) and introduced two sequence - level variants of knowledge distillation , which provide improvements over standard word - level knowledge distillation . We have chosen to focus on translation as this domain has generally required the largest capacity deep learning models , but the sequence - to - sequence framework has been successfully applied to a wide range of tasks including parsing , summarization , dialogue , NER / POS - tagging , image captioning , video generation , and speech recognition . We anticipate that methods described in this paper can be used to similarly train smaller models in other domains . The exploration of generative models , deep reinforcement learning as well as the evolution of sequence to sequence paradigms also represent important milestones in the field . Deep learning is now being deployed in a wide range of domains , including bio - informatics , computer vision , machine translation , dialogue systems and natural language processing , just to name a few . Another field that has been transformed by this technology is Automatic Speech Recognition ( ASR ) . In turn , these assistants allow for natural , personalized interactions with users by tailoring natural language system responses to the dialogue context . Dialogue state tracking ( DST ) is a crucial part of dialogue systems young2013POMDPDialogueReview . In DST , a dialogue state tracker estimates the state of the conversation using the current user utterance and the conversation history . Natural language processing ( NLP ) requires varieties of knowledge . When we consider about man-machine dialogue we have to prepare lots of knowledge , and also strong inference functions such as logical inference and common sense reasoning . In this paper the author explains some new developments in knowledge for computational linguistics , then discuss about what kind of knowledge is required for a dialogue system . A range of UIMS are examined and the problems that their development has addressed . Intelligent dialogue management is discussed with reference to several systems that handle the problems of specification and implementation in a variety of ways . Finally , the major outstanding issues in intelligent presentation dialogue management are identified . There are many different kinds of context information , with different demands on representation . In this paper we discuss the representation of the context information needed to support intelligent dialogue management in interactive speech systems . We argue that simple types of context information should be represented in a very simple form to allow efficient processing , while other types of context information require sophisticated logics for articulate representation and reasoning . Recently , the back-channel information is included in the framework of dialogue act tagging in the game-playing dialogue[3 ] and meetings[4 ] . In the information navigation dialogue , in which an expert guide presents a list of recommendation spots , it is expected that the prosodic pattern of the back-channel conveys the para-linguistic information , that is , it suggests the positive/negative feeling on the recommended candidate . We also presume that the human expert guide detects such feelings expressed via back-channels , and chooses to continue the explanation of the current topic if the user seems interested , or change the topic otherwise . Background Health Literacy has become an important issue area during the past decades , both in the academic and public dialogue . At the same time , new possibilities in the emerging field of personal genomics and the interpretation of health-related genetic information create seemingly extraordinary benefits and - for some - underestimated risks and disadvantages of epic proportions . There is a strong trend towards multilingualism in Flemish and Dutch films today . In order to make such films accessible for a blind and visually impaired audience , the audio description ( AD ) , which supplies the information from the visuals that can not be accessed by this target audience , must be combined with audio subtitling ( AST ) , for the translation of the dialogue . Today , a wide variety of strategies is used to accomplish this form of textual manipulation , but current practice is largely based on intuition . This paper describes a symbolic model of rational action and decision making to support analysing dialogue . The model approximates principles of behaviour from game theory , and its proof theory makes Gricean principles of cooperativity derivable when the agents ’ preferences align . The traditional dialogue system pipeline is not flexible enough to address the needs of highly interactive systems , which include parallel processing of multimodal input and output . We present an architectural solution for a multimodal conversational social dialogue system . This is a leading idea of all the recent task-driven and question-driven models of contextual dynamics ( Allen , 1991 ; Hobbs et al. , 1993 ; Roberts , 1996 , 2004 ; Ginzburg , 1995b , 1996 ; Groenendijk , 1999 ; Beaver , 2002 ; Buring , 2003 ; Stone et al. , 2007 ; Beaver & Clark , 2008 ; Groenendijk & Roelofsen , 2009 ) as well as their game-theoretic and decision-theoretic counterparts ( Clark , 1996 ; Merin , 1997 , 1999 ; Parikh , 2000 , 2001 ; van Rooy , 2003 ; Benz , 2005 ; Franke , 2009 ; Jager , To appear ; Frank & Goodman , 2012 ) . The goal of this paper is to develop and motivate general techniques for identifying and characterizing this kind of task dependence in real task-oriented dialogue , where the phenomena can be both quantitatively and qualitatively assessed . The basis for my exploration is the Cards corpus , a large collection of dialogues derived from a two-person collaborative game . This type of dialogue is desirable in online learning environments , since training learners to use it ha s been shown to improve learning outcomes . In this paper , we frame t he problem of exploratory dialogue detection as a binary class ification task , classifying a given contribution to an online dia logue as exploratory or non-exploratory . We propose a self-trainin g framework to identify exploratory dialogue . An adaptive NLG policy learned in our framework shows a statistically significant 27 % relative increase in reward over an “ RL-majority ” baseline policy for the same task . We thereby also show that that such NLG problems should be approached in combination with dialogue management decisions , and we show how to jointly optimize NLG and dialogue management plans . Thus we decided to investigate more cognitive oriented models of memory . We hypothesize that combining machine learning approaches in an adversarial setup with a new memory model for the agent would result in improved performances for goal-oriented dialog systems . MOTS-CLES : systemes de dialogue , apprentissage par renforcement , modele de memoire , appren-tissage par antagonisme , systemes orientes-buts . This article describes a text-based , open-domain dialog system developed especially for social , smalltalk-like conversations . While much research is focused on goal-oriented dialog currently , in human-to-human communication many dialogs do not have a predefined goal . In order to achieve similar communication with a computer , we propose a framework which is easily extensible by combining different response patterns . In goal-driven dialogue systems , success is often defined based on a structured definition of the goal . This requires that the dialogue system be constrained to handle a specific class of goals and that there be a mechanism to measure success with respect to that goal . Data-driven dialogue system development has to be adapted to meet this requirement --- therefore , reducing the amount of data and annotations necessary for training such systems is a central research problem . In this paper , we present the Dialogue Knowledge Transfer Network ( DiKTNet ) , a state-of-the-art approach to goal-oriented dialogue generation which only uses a few example dialogues ( i.e. few-shot learning ) , none of which has to be annotated . We achieve this by performing a 2-stage training . This thesis describes a model for goal-directed dialogue and activity control in real-time for multiple conversation participants that can be human users or virtual characters in multimodal dialogue systems and a framework implementing the model . It is concerned with two genres : task-oriented systems and interactive narratives . Using stateof-the-art machine learning techniques spanning from natural language processing and speech recognition to reinforcement and deep learning , these chatbots have become more lifelike and harder to discern from humans . Recent work in conversational agents has been focused on goal-directed dialogue focused on closed domains such as appointment setting , bank information requests , question-answering , and movie discussion . Natural human conversations are seldom limited in scope and jump from topic to topic , they are laced with metaphor and subtext and face-to-face communication is supplemented with non-verbal cues . A self-serve multi-domain goal oriented dialogue platform . We demonstrate the Task Completion Platform ( TCP ) ; a multi-domain dialogue platform that can host and execute large numbers of goal-orientated dialogue tasks . The platform features a task configuration language , TaskForm , that allows the definition of each individual task to be decoupled from the overarching dialogue policy used by the platform to complete those tasks . In a goal-oriented spoken dialog system , the major aim of language understanding is to classify utterances into one or more of the pre-defined intents and extract the associated named entities . Typically , the intents are designed by a human expert according to the application domain . The questioner figures out the answerer 's intent via selecting a plausible question by explicitly calculating the information gain of the candidate intentions and possible answers to each question . We test our framework on two goal-oriented visual dialogue tasks : " MNIST Counting Dialog " and " GuessWhat?!. " In our experiments , AQM outperforms comparative algorithms and makes human-like dialogue . We further use AQM as a tool for analyzing the mechanism of deep reinforcement learning approach and discuss the future direction of practical goal-oriented neural dialogue systems . Due to the business process nature of these conversations , end-to-end machine learning systems are generally not a viable option , as the generated dialogue agents must be deployable and verifiable on behalf of the businesses authoring them . In this work , we propose a paradigm shift in the creation of goal-oriented complex dialogue systems that dramatically eliminates the need for a designer to manually specify a dialogue tree , which nearly all current systems have to resort to when the interaction pattern falls outside standard patterns such as slot filling . We propose a declarative representation of the dialogue agent to be processed by state-of-the-art planning technology . This paper presents a new method - adversarial advantage actor-critic ( Adversarial A2C ) , which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems . Inspired by generative adversarial networks ( GAN ) , we train a discriminator to differentiate responses/actions generated by dialogue agents from responses/actions by experts . For example , downstream modules are affected by earlier modules , and the performance of the entire system is not robust to the accumulated errors . This paper presents a novel end-to-end learning framework for task-completion dialogue systems to tackle such issues . Our neural dialogue system can directly interact with a structured database to assist users in accessing information and accomplishing certain tasks . This paper presents a Discriminative Deep Dyna-Q ( D3Q ) approach to improving the effectiveness and robustness of Deep Dyna-Q ( DDQ ) , a recently proposed framework that extends the Dyna-Q algorithm to integrate planning for task-completion dialogue policy learning . To obviate DDQ 's high dependency on the quality of simulated experiences , we incorporate an RNN-based discriminator in D3Q to differentiate simulated experience from real user experience in order to control the quality of training data .
Since the voice fundamental frequency ( F 0 ) contour is an important acoustic correlate of many prosodic constructs , modeling and analyzing F 0 contours can potentially be useful for many speech applications such as speech synthesis , speaker identification , speech conversion , and dialogue systems , in which prosodic information plays a significant role . In this chapter , we formulate a statistical model of F 0 contours by translating the “ Fujisaki model , ” a well-founded mathematical model representing the control mechanism of vocal fold vibration , into a probabilistic model described as a discrete-time stochastic process .
Analysis of speech acts and discourse structures is essential to a dialogue understanding system because speech acts and discourse structures are closely tied with the speaker ’s intention . However , it has been difficult to infer a speech act and a discourse structure from a surface utterance because they highly depend on the context of the utterance .
Representing large lexical data bases , treating sophisticated phonological and morphological transformations , and processing in real time large corpuses demands fast finite-state methods toolkits . Analysing the syntactic structure , computing anaphoric relations , and dealing with the representation of information flow in dialogue understanding , demands the processing of complex constraints on graph structures , with sophisticated sharing of large non-deterministic search spaces .
In customer service ( CS ) , customers pose questions that will be answered by customer service staff , and the communication in CS is a typical multi-round conversation . However , there are no explicit correspondences among conversational utterances , and obtaining the explicit alignments of those utterances not only contributes to dialogue analysis but also provides valuable data for learning intelligent dialogue systems . In this paper , we first present a study on utterance alignment ( UA ) in CS .
The importance of the Internet grows dramatically from year to year in more or less all disciplines . This volume contains the papers ( 12 contributions in English and 6 in German ) read at an international meeting of the IADA ( International Association for Dialogue Analysis , Bologna ) in Erlangen , April 2 3 , 1998 . There were two sections at the conference , one dealt with the new means of electronic communication , mainly with E-Mail communication and with Internet Relay Chat ( IRC ) , the other , more traditional , dealt with dialogues on television and the radio , or with specific aspects of Mass Media communication . "
The article begins by explaining how the dialogues were created and then discusses the benefits and potential shortcomings of this approach in the context of interpreter education . The main points of discussion concern ( 1 ) the level of systematicity and authenticity that can be achieved with this corpus-based approach ; and ( 2 ) the potential of a 3D virtual environment to increase this sense of authenticity and thus to enable students to experience the essence of dialogue interpreting in a simulated environment .
Anchoring dialogue interpreting in principles of teaching and learning . In today ’s multilingual societies cross-linguistic communication is increasingly frequent , especially when it relates to accessing services .
It is shown that , when communicative functions are assigned to functional segments , thereby eliminating every form of segmentation-related multifunctionality , an average multifunctionality is found between 1.8 and 3.6 , depending on what is considered to count as a segment 's communicative function . Moreover , a good understanding of the nature of the relations among the various multiple functions that a segment may have , and of the relations between functional segments and other units in dialogue segmentation , opens the way for defining a multidimensional computational update semantics for dialogue interpretation .
Dialogue Act ( DA ) classification is a challenging problem in dialogue interpretation , which aims to attach semantic labels to utterances and characterize the speaker ’s intention . Currently , many existing approaches formulate the DA classification problem ranging from multi-classification to structured prediction , which suffer from two limitations : a ) these methods are either handcrafted feature-based or have limited memories .
The features of OntoVPA are offered as generic solutions to core DM problems , such as dialogue state tracking , anaphora and coreference resolution , etc . To the best of our knowledge , OntoVPA is the first commercially available , fully implemented DMS that employs ontologies and ontology-based rules for ( a ) domain model representation and reasoning , ( b ) dialogue representation and state tracking , and ( c ) response generation . OntoVPA is a declarative , knowledge-based system which can be customized to a new VPA domain by modifying and exchanging ontologies and rule bases , with very little to no conventional programming required .
The advent of VoiceXML for voice applications contributed to reduce the proliferation of incompatible dialogue interpreters , but introduced new complexity . As a response to these issues , we designed an architecture for dialogue representation and interpretation , AdaRTE , which allows developers to layout dialogue interactions through a high level formalism that offers both declarative and procedural features . AdaRTE aim is to provide a ground for deploying complex and adaptable dialogues whilst allows the experimentation and incremental adoption of innovative speech technologies .
The advent of VoiceXML reduced the proliferation of incompatible dialog formalisms , at the expense of adding even more complexity . This paper introduces a novel architecture for dialogue representation and interpretation , AdaRTE , which allows developers to lay out dialog interactions through a high-level formalism , offering both declarative and procedural features . AdaRTE 's aim is to provide a ground for deploying complex and adaptable dialogs whilst allowing experimentation and incremental adoption of innovative speech technologies .
The aim of the project was to improve the quality of synthesised speech output to be used in dialogue systems as an additional element of multimodal man-machine interfaces . German text and dialogue interaction analysis ( theoretical research ) has been carried out to predict the tone groups ( TGs ) , the phrase boundaries in sentences and the place of the focus in the phrase . Tone groups represent the general intonation structure of the phrase not taking into account word level intonation .
The resulting logic corresponds closely to the infinitevalued logic Łα , of Łukasiewicz . In fact , the approach provides a dialogue interpretation of Ł∞ and leads to a convenient method for establishing logical identities . Set theory is then developed , not by taking set as a primitive concept but by assuming each set A is determined by a property P characteristic of its members : A = {x : P(x)}. When this is expressed formally the result can be read in two ways according to whether the underlying logic is classical logic or Ł∞ ( with the above interpretation ) .
With the implementation of authentic assessment toward the students who participate in the drama appreciation course . The obtained results show significant increase in their competence through the 6 kinds of task which contain website based authentic assessment on the stage of manipulating expression , scripting , dialogue reading , pre performing and stage performance .
In this work we have investigated existing knowledge distillation methods for NMT ( which work at the word - level ) and introduced two sequence - level variants of knowledge distillation , which provide improvements over standard word - level knowledge distillation . We have chosen to focus on translation as this domain has generally required the largest capacity deep learning models , but the sequence - to - sequence framework has been successfully applied to a wide range of tasks including parsing , summarization , dialogue , NER / POS - tagging , image captioning , video generation , and speech recognition . We anticipate that methods described in this paper can be used to similarly train smaller models in other domains .
The exploration of generative models , deep reinforcement learning as well as the evolution of sequence to sequence paradigms also represent important milestones in the field . Deep learning is now being deployed in a wide range of domains , including bio - informatics , computer vision , machine translation , dialogue systems and natural language processing , just to name a few . Another field that has been transformed by this technology is Automatic Speech Recognition ( ASR ) .
In turn , these assistants allow for natural , personalized interactions with users by tailoring natural language system responses to the dialogue context . Dialogue state tracking ( DST ) is a crucial part of dialogue systems young2013POMDPDialogueReview . In DST , a dialogue state tracker estimates the state of the conversation using the current user utterance and the conversation history .
Natural language processing ( NLP ) requires varieties of knowledge . When we consider about man-machine dialogue we have to prepare lots of knowledge , and also strong inference functions such as logical inference and common sense reasoning . In this paper the author explains some new developments in knowledge for computational linguistics , then discuss about what kind of knowledge is required for a dialogue system .
A range of UIMS are examined and the problems that their development has addressed . Intelligent dialogue management is discussed with reference to several systems that handle the problems of specification and implementation in a variety of ways . Finally , the major outstanding issues in intelligent presentation dialogue management are identified .
There are many different kinds of context information , with different demands on representation . In this paper we discuss the representation of the context information needed to support intelligent dialogue management in interactive speech systems . We argue that simple types of context information should be represented in a very simple form to allow efficient processing , while other types of context information require sophisticated logics for articulate representation and reasoning .
Recently , the back-channel information is included in the framework of dialogue act tagging in the game-playing dialogue[3 ] and meetings[4 ] . In the information navigation dialogue , in which an expert guide presents a list of recommendation spots , it is expected that the prosodic pattern of the back-channel conveys the para-linguistic information , that is , it suggests the positive/negative feeling on the recommended candidate . We also presume that the human expert guide detects such feelings expressed via back-channels , and chooses to continue the explanation of the current topic if the user seems interested , or change the topic otherwise .
Background Health Literacy has become an important issue area during the past decades , both in the academic and public dialogue . At the same time , new possibilities in the emerging field of personal genomics and the interpretation of health-related genetic information create seemingly extraordinary benefits and - for some - underestimated risks and disadvantages of epic proportions .
There is a strong trend towards multilingualism in Flemish and Dutch films today . In order to make such films accessible for a blind and visually impaired audience , the audio description ( AD ) , which supplies the information from the visuals that can not be accessed by this target audience , must be combined with audio subtitling ( AST ) , for the translation of the dialogue . Today , a wide variety of strategies is used to accomplish this form of textual manipulation , but current practice is largely based on intuition .
This paper describes a symbolic model of rational action and decision making to support analysing dialogue . The model approximates principles of behaviour from game theory , and its proof theory makes Gricean principles of cooperativity derivable when the agents ’ preferences align .
The traditional dialogue system pipeline is not flexible enough to address the needs of highly interactive systems , which include parallel processing of multimodal input and output . We present an architectural solution for a multimodal conversational social dialogue system .
This is a leading idea of all the recent task-driven and question-driven models of contextual dynamics ( Allen , 1991 ; Hobbs et al. , 1993 ; Roberts , 1996 , 2004 ; Ginzburg , 1995b , 1996 ; Groenendijk , 1999 ; Beaver , 2002 ; Buring , 2003 ; Stone et al. , 2007 ; Beaver & Clark , 2008 ; Groenendijk & Roelofsen , 2009 ) as well as their game-theoretic and decision-theoretic counterparts ( Clark , 1996 ; Merin , 1997 , 1999 ; Parikh , 2000 , 2001 ; van Rooy , 2003 ; Benz , 2005 ; Franke , 2009 ; Jager , To appear ; Frank & Goodman , 2012 ) . The goal of this paper is to develop and motivate general techniques for identifying and characterizing this kind of task dependence in real task-oriented dialogue , where the phenomena can be both quantitatively and qualitatively assessed . The basis for my exploration is the Cards corpus , a large collection of dialogues derived from a two-person collaborative game .
This type of dialogue is desirable in online learning environments , since training learners to use it ha s been shown to improve learning outcomes . In this paper , we frame t he problem of exploratory dialogue detection as a binary class ification task , classifying a given contribution to an online dia logue as exploratory or non-exploratory . We propose a self-trainin g framework to identify exploratory dialogue .
An adaptive NLG policy learned in our framework shows a statistically significant 27 % relative increase in reward over an “ RL-majority ” baseline policy for the same task . We thereby also show that that such NLG problems should be approached in combination with dialogue management decisions , and we show how to jointly optimize NLG and dialogue management plans .
Thus we decided to investigate more cognitive oriented models of memory . We hypothesize that combining machine learning approaches in an adversarial setup with a new memory model for the agent would result in improved performances for goal-oriented dialog systems . MOTS-CLES : systemes de dialogue , apprentissage par renforcement , modele de memoire , appren-tissage par antagonisme , systemes orientes-buts .
This article describes a text-based , open-domain dialog system developed especially for social , smalltalk-like conversations . While much research is focused on goal-oriented dialog currently , in human-to-human communication many dialogs do not have a predefined goal . In order to achieve similar communication with a computer , we propose a framework which is easily extensible by combining different response patterns .
In goal-driven dialogue systems , success is often defined based on a structured definition of the goal . This requires that the dialogue system be constrained to handle a specific class of goals and that there be a mechanism to measure success with respect to that goal .
Data-driven dialogue system development has to be adapted to meet this requirement --- therefore , reducing the amount of data and annotations necessary for training such systems is a central research problem . In this paper , we present the Dialogue Knowledge Transfer Network ( DiKTNet ) , a state-of-the-art approach to goal-oriented dialogue generation which only uses a few example dialogues ( i.e. few-shot learning ) , none of which has to be annotated . We achieve this by performing a 2-stage training .
This thesis describes a model for goal-directed dialogue and activity control in real-time for multiple conversation participants that can be human users or virtual characters in multimodal dialogue systems and a framework implementing the model . It is concerned with two genres : task-oriented systems and interactive narratives .
Using stateof-the-art machine learning techniques spanning from natural language processing and speech recognition to reinforcement and deep learning , these chatbots have become more lifelike and harder to discern from humans . Recent work in conversational agents has been focused on goal-directed dialogue focused on closed domains such as appointment setting , bank information requests , question-answering , and movie discussion . Natural human conversations are seldom limited in scope and jump from topic to topic , they are laced with metaphor and subtext and face-to-face communication is supplemented with non-verbal cues .
A self-serve multi-domain goal oriented dialogue platform . We demonstrate the Task Completion Platform ( TCP ) ; a multi-domain dialogue platform that can host and execute large numbers of goal-orientated dialogue tasks . The platform features a task configuration language , TaskForm , that allows the definition of each individual task to be decoupled from the overarching dialogue policy used by the platform to complete those tasks .
In a goal-oriented spoken dialog system , the major aim of language understanding is to classify utterances into one or more of the pre-defined intents and extract the associated named entities . Typically , the intents are designed by a human expert according to the application domain .
The questioner figures out the answerer 's intent via selecting a plausible question by explicitly calculating the information gain of the candidate intentions and possible answers to each question . We test our framework on two goal-oriented visual dialogue tasks : " MNIST Counting Dialog " and " GuessWhat?!. " In our experiments , AQM outperforms comparative algorithms and makes human-like dialogue . We further use AQM as a tool for analyzing the mechanism of deep reinforcement learning approach and discuss the future direction of practical goal-oriented neural dialogue systems .
Due to the business process nature of these conversations , end-to-end machine learning systems are generally not a viable option , as the generated dialogue agents must be deployable and verifiable on behalf of the businesses authoring them . In this work , we propose a paradigm shift in the creation of goal-oriented complex dialogue systems that dramatically eliminates the need for a designer to manually specify a dialogue tree , which nearly all current systems have to resort to when the interaction pattern falls outside standard patterns such as slot filling . We propose a declarative representation of the dialogue agent to be processed by state-of-the-art planning technology .
This paper presents a new method - adversarial advantage actor-critic ( Adversarial A2C ) , which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems . Inspired by generative adversarial networks ( GAN ) , we train a discriminator to differentiate responses/actions generated by dialogue agents from responses/actions by experts .
For example , downstream modules are affected by earlier modules , and the performance of the entire system is not robust to the accumulated errors . This paper presents a novel end-to-end learning framework for task-completion dialogue systems to tackle such issues . Our neural dialogue system can directly interact with a structured database to assist users in accessing information and accomplishing certain tasks .
This paper presents a Discriminative Deep Dyna-Q ( D3Q ) approach to improving the effectiveness and robustness of Deep Dyna-Q ( DDQ ) , a recently proposed framework that extends the Dyna-Q algorithm to integrate planning for task-completion dialogue policy learning . To obviate DDQ 's high dependency on the quality of simulated experiences , we incorporate an RNN-based discriminator in D3Q to differentiate simulated experience from real user experience in order to control the quality of training data .
Probabilistic Modeling of Pitch Contours Toward Prosody Synthesis and Conversion An Integrated Dialogue Analysis Model for Determining Speech Acts and Discourse Structures Zen and the Art of Symbolic Computing: Light and Fast Applicative Algorithms for Computational Linguistics Utterance Alignment in Custom Service by Integer Programming Dialogue analysis and the mass media : proceedings of the international conference, Erlangen, April 2-3, 1998 Populating a 3D virtual learning environment for interpreting students with bilingual dialogues to support situated learning in an institutional context Chapter 1. Anchoring dialogue interpreting in principles of teaching and learning Multifunctionality in dialogue Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training OntoVPA - An Ontology-Based Dialogue Management System for Virtual Personal Assistants AdaRTE: Adaptable Dialogue Architecture and Runtime Engine. A New Architecture for Health-Care Dialogue Systems Adaptable dialog architecture and runtime engine (AdaRTE): A framework for rapid prototyping of health dialog systems Prosody generation for German CTS/TTS systems (from theoretical intonation patterns to practical realisation) Łukasiewicz logic and fuzzy set theory Enhancing Students’ Drama Performance: A Process Approach Sequence-Level Knowledge Distillation Light Gated Recurrent Units for Speech Recognition Global-Locally Self-Attentive Dialogue State Tracker Natural language processing and knowledge Intelligent Presentation and Dialogue Management Context Representation for Dialogue Management Detection of feeling through back-channels in spoken dialogue Reading Your Genome Audio Description with Audio Subtitling for Dutch Multilingual Films: Manipulating Textual Cohesion on Different Levels A Cognitive Model for Conversation How was your day? An architecture for multimodal ECA systems Goal-Driven Answers in the CardsDialogue Corpus A self-training framework for automatic identification of exploratory dialogue Adaptive natural language generation in dialogue using reinforcement learning Les systèmes de dialogue orientés-but : état de l'art et perspectives d'amélioration Towards an Open-Domain Social Dialog System Predicting Success in Goal-Driven Human-Human Dialogues Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks A flexible and reusable framework for dialogue and action management in multi-party discourse Improvised Theatre Alongside Artificial Intelligences Task Completion Platform: A self-serve multi-domain goal oriented dialogue platform Semi-supervised learning for spoken language understanding semantic role labeling Answerer in Questioner's Mind for Goal-Oriented Visual Dialogue Planning for Goal-Oriented Dialogue Systems Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning End-to-End Task-Completion Neural Dialogue Systems Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning
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["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"]
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PADL CCL Dialogue Analysis and the Mass Media: Proceedings of the International Conference, Erlangen, April 2-3, 1998 BigData IWSDS MedInfo EMNLP IEEE Transactions on Emerging Topics in Computational Intelligence ACL Advanced Topics in Artificial Intelligence CONTEXT INTERSPEECH SIGDIAL Conference IWSDS SIGDIAL Conference EMNLP/IJCNLP (1) AIIDE HLT-NAACL Demos IJCNLP(1) EMNLP
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