A well modeled discourse is the fundamental work of building a dialogue system within a problem domain . The detail of the modeling process could be different from domain to domain . AUTHOR Bardovi-Harlig , Kathleen ; Hartford , Beverly S. TITLE Refining the DCT : Comparing Open Questionnaires and Dialogue Completion Tasks . PUB DATE 93 NOTE 25p . Using autoencoders allows handling a large variety of data , such as images , text or even dialogues . Our experiments show the flexibility of the proposed approach in dealing with different types of data in different settings : images with CIFAR-10 and CIFAR-100 ( not-in-training setting ) , text with Amazon reviews ( PU learning ) and dialogues with Facebook bAbI ( next response classification and dialogue completion ) . However , it heavily relies on the distribution of entities across adjacent sentences but ignores the emotional context embedded in non-entity text and fails to model long dependencies between speech intentions . These limitations become even more severe when applied to dialogue domain since sentences in dialogue are short , informal and colloquial , thereby , less entities could be extracted and less coherence information could be expressed in these grids . To address the limitations of entity gird methods and incorporate the structure knowledge of dialogue , we propose a new neural network architecture , Hierarchical Intention Enhance Network , to integrate semantic context and speech intention in both utterance and dialogue levels to hierarchically model the global coherence without any entity grids . In recent years , dialogue optimization using reinforcement learning has evolved to be a state-of-the-art technique . The primary focus of research in the dialogue domain is to learn some optimal policy with regard to the task description ( reward function ) and the user simulation being employed . However , in case of human-human interaction , the parties involved in the dialogue conversation mutually evolve over the period of interaction . In particular , we present a formalisation of the dialogue gameboard introduced by Jonathan Ginzburg . From a more general point of view , we show that DL is particularly well suited to develop rigorous formal foundations for an approach to dialogue dynamics based on information state updates . Conversational agents offer users a naturallanguage interface to accomplish tasks , entertain themselves , or access information . Informational dialogue is particularly challenging in that the agent has to hold a conversation on an open topic , and to achieve a reasonable coverage it generally needs to digest and present unstructured information from textual sources . Making responses based on such sources sound natural and fit appropriately into the conversation context is a topic of ongoing research , one of the key issues of which is preventing the agent ’s responses from sounding repetitive . Conversational telephone speech ( CTS ) collections of Arabic dialects distributed trough the Linguistic Data Consortium ( LDC ) provide an invaluable resource for the development of robust speech systems including speaker and speech recognition , translation , spoken dialogue modeling , and information summarization . They are frequently relied on also in language ( LID ) and dialect identification ( DID ) evaluations . Both the linear chain CRF model and the LSTM-CRF model outperform the naı̈ve baselines by a large margin , and LSTM-CRF has an edge between the two . To our knowledge , this is the first study on dialogue segment annotation using neural network models . This study is also a stepping-stone to automating the microgenetic analysis of cognitive interactions between students . However , the conversational complexities demand building more personalized and user-oriented dialogue process systems . To support human-computer dialogue process many models have been proposed . Considering personalization aspect , this research work presents novel Context-aware Dialogue Manager ( CADM ) model with its foundation based on well-known JDL fusion model . In this paper , we propose a novel hierarchical variational memory network ( HVMN ) , by adding the hierarchical structure and the variational memory network into a neural encoder-decoder network . By emulating human-to-human dialogues , our proposed method can capture both the high-level abstract variations and long-term memories during dialogue tracking , which enables the random access of relevant dialogue histories . Extensive experiments conducted on three large real-world datasets verify a significant improvement of our proposed model against state-of-the-art baselines for dialogue generation . For establishing communications and modeling a framework for dialogue between intelligent agents , it is beneficial to have a well-defined strategy . In a dialogue run , the strategies are the manners to achieve the agents ’ goals by selecting the best option between the different existent choices . There are not many works which define the strategies for dialogue between agents . While most works approach it as a simple categorization problem , sentiment analysis is actually a suitcase research problem that requires tackling many NLP tasks , including word polarity disambiguation , subjectivity detection , personality recognition , microtext normalization , concept extraction , time tagging , and aspect extraction . Sentiment analysis has raised growing interest both within the scientific community , leading to many exciting open challenges , as well as in the business world , due to the remarkable benefits to be had from financial and political forecasting , e - health and e - tourism , user profiling and community detection , manufacturing and supply chain applications , human communication comprehension and dialogue systems , etc . In the field of emotion recognition , early works by and showed that fusion of audio and visual systems , creating a bimodal signal , yielded a higher accuracy than any unimodal system . Neural network language models have played a central role in recent natural language processing ( NLP ) advances . For example , neural encoder - decoder models , which were successfully applied to various natural language generation tasks including machine translation , summarization , and dialogue , can be interpreted as conditional neural language models . Neural language models also positively influence syntactic parsing . Sequence generation and prediction tasks span many modes of data , ranging from audio and language modelling , to more general timeseries prediction tasks . Applications of such models include speech recognition , machine translation , dialogue generation , speech synthesis , forecasting , and music generation , among others . Neural networks can be applied to these tasks by predicting sequence elements one - by - one , conditioning on the history of sequence elements , forming an autoregressive model . Sequential recurrent neural networks ( RNNs ) are remarkably effective models of natural language . In the last few years , language model results that substantially improve over long - established state - of - the - art baselines have been obtained using RNNs as well as in various conditional language modeling tasks such as machine translation , image caption generation , and dialogue generation . Despite these impressive results , sequential models are a priori inappropriate models of natural language , since relationships among words are largely organized in terms of latent nested structures rather than sequential surface order . 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 . The dialogue system then uses this estimated state to plan the next action and respond to the user . We show that by doing so , GLAD generalizes on rare slot - value pairs with few training examples . GLAD achieves state - of - the - art results of 88.1 % goal accuracy and 97.1 % request accuracy on the WoZ dialogue state tracking task wen2017NetworkBasedEndToEndDialogueSystem , outperforming prior best by 3.7 % and 5.5 % . On DSTC2 dstc2 , we achieve 74.5 % goal accuracy and 97.5 % request accuracy , outperforming prior best by 1.1 % and 1.0 % . subsection : Dataset The Dialogue Systems Technology Challenges ( DSTC ) provides a common framework for developing and evaluating dialogue systems and dialogue state trackers . Under this framework , dialogue semantics such as states and actions are based on a task ontology such as restaurant reservation . section : Related Work Dialogue State Tracking . Traditional dialogue state trackers rely on a separate SLU component that serves as the initial stage in the dialogue agent pipeline . The downstream tracker then combines the semantics extracted by the SLU with previous dialogue context in order to estimate the current dialogue state . Recent results in dialogue state tracking show that it is beneficial to jointly learn speech understanding and dialogue tracking . These approaches directly take as input the N - best list produced by the ASR system . In recent years , several models have been proposed to integrate the language and vision modalities ; usually the integration is operationalized by element - wise multiplication between linguistic and visual vectors . Though the interest in these modalities has spread in an astonishing way thanks to various multimodal tasks proposed , including the IC , VQA , Visual Reasoning and Visual Dialogue tasks mentioned above , very little work has been done on grounding entailment . Interestingly , youn : We show that our method leads to improved performance on both a synthetic task , and for German - English machine translation . Our analysis paves the way for such methods to be applied in natural language generation tasks , such as machine translation , caption generation , and dialogue modelling . section : Introduction This work serves our larger goal of identifying patterns of tutors ’ actions , in the form of dialogue acts , that relate to learning . The preliminary results we obtained for dialogue act classification using a machine learning approach are promising . This paper presents an unsupervised dialogue act modeling approach that leverages the influence of learner characteristics , particularly students ’ perceptions of their own skill , on their language use . The experimental findings show that leveraging skill perception within dialogue act classification improves performance of the models , producing better accuracy . This line of investigation will inform the design of nextgeneration tutorial dialogue systems , which leverage machinelearned models to adapt to their users . This paper describes an approach in which phrase accent information is used for dialogue act recogniti on in German spontaneous speech This application is an example of how automatically computed pros odic information can be used in automatic speech re cognition Usually the important intention conveyed by an utterance is found in the focused area which is often accentuated When all the words of an ut terance are used for dialogue act classi cation the best result is achieved only if all probabilities e g of n grams are known In real life applications this not the case Because utterances can be very similar to one another but belong to di erent dialogue act classes it may be possible to distinguish the classes on the basis of characteristic words For this reason dialogue act classi cation is often based on keyword detection The selection of keywords is crucial Bet ter recognition relies on better chosen keywords This paper shows how keyword selection can be improved by using two additional information sources lexical POS information and prosody POS and prosodic in formation is used to build subsets of the vocabulary to improve recognition Experiments are conducted on a sub sample of the VERBMOBIL corpus The aim is to distinguish between four dialogue act sub classes of the general class SUGGEST The emergency type should be decided with insufficient information and the decided type leads to the next information . In this paper , we propose a model of dialogue classification to determine the objective of the dialogue while the conversation is on its way . Situation of the conversation is used for decision making . Finally , we propose a novel study that evaluates the impact of the Word Error Rate ( WER ) in the LDA topic space learning process as well as during the theme identification task . This original qualitative study points out that selecting a small subset of words having the lowest WER ( instead of using all the words ) allows the system to better classify automatic transcriptions with an absolute gain of 0.9 point , in comparison to the best performance achieved on this dialogue classification task ( precision of 83.3 % ) . We look at the effect of using high level discourse knowledge in dialogue act type detection . We also look at ways this knowledge can be used for improving language modelling and intonation modelling of utterance types . While this is easy for humans , the present neural methods that rely on learned word embeddings may not perform well for these NLP tasks , especially in the presence of Out-Of-Vocabulary ( OOV ) or rare NEs . In this paper , we propose a solution for this problem , and present empirical evaluations on : a ) a structured Question-Answering task , b ) three related Goal-Oriented dialog tasks , and c ) a Reading-Comprehension task , which show that the proposed method can be effective in dealing with both in-vocabulary and OOV NEs . We create extended versions of dialog bAbI tasks 1,2 and 4 and OOV versions of the CBT test set available at - https://github.com/IBM/ne-table-datasets . The questioner figures out the answerer ’s intention 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 dialog tasks : MNIST Counting Dialog ' ' and GuessWhat?!. ' ' In our experiments , AQM outperforms comparative algorithms by a large margin . However , these models require a large corpus of dialogues to learn effectively . For goal-oriented dialogues , such datasets are expensive to collect and annotate , since each task involves a separate schema and database of entities . Further , the Wizard-of-Oz approach commonly used for dialogue collection does not provide sufficient coverage of salient dialogue flows , which is critical for guaranteeing an acceptable task completion rate in consumer-facing conversational agents . Developing agents to engage in complex goal-oriented dialogues is challenging partly because the main learning signals are very sparse in long conversations . In this paper , we propose a divide-and-conquer approach that discovers and exploits the hidden structure of the task to enable efficient policy learning . We demonstrate the genericity of wide domain models , such as broadcast news acoustic and language models , and techniques to achieve a higher degree of genericity , such as transparent methods to adapt such models to a specific task . This work targets three tasks using commonly available corpora : small vocabulary recognition ( TI-digits ) , text dictation ( WSJ ) , and goal-oriented spoken dialog ( ATIS ) . Since then , the various contributors have obviously worked at adding tutorial and review material to the chapters to supplement the research reports . This is very useful , especially since the primary effect of the book will be to promote the idea that interactive software systems can eventually become intelligent partners in goal-directed dialogs with humans . The book is by no means an engineering handbook ; it is primarily theoretical , but includes sketches of requirements specifications and feasibility demonstrations . They learn with the assumption that at any time there is only one correct next utterance . In this work , we focus on this problem in the goal-oriented dialog setting where there are different paths to reach a goal . We propose a new method , that uses a combination of supervised learning and reinforcement learning approaches to address this issue . The task of Next Utterance Classification in dialog learning highly resembles that of Question Answering , but there has not been much attention to applying models across the two fields , especially not in more practical dialog modeling scenarios . Hence , this paper presents end-to-end Question Answering ( QA ) style models for the goal-oriented dialog system task in Dialog System Technology Challenges ( DSTC ) 7 . We first provide a comprehensive quantitative and qualitative analysis of the newly introduced Advising dataset and show the heavy reliance of the data on an external Knowledge Base ( KB ) of course offerings . The noetic end-to-end response selection challenge as one track in Dialog System Technology Challenges 7 ( DSTC7 ) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems , for which participants need to select the correct next utterances from a set of candidates for the multi-turn context . This paper describes our systems that are ranked the top on both datasets under this challenge , one focused and small ( Advising ) and the other more diverse and large ( Ubuntu ) . Vast improvements in natural language understanding and speech recognition have paved the way for conversational interaction with computers . While conversational agents have often been used for short goal-oriented dialog , we know little about agents for developing computer programs . To explore the utility of natural language for programming , we conducted a study ( $ n$=45 ) comparing different input methods to a conversational programming system we developed . Multiple access to the external memory to find out necessary information through a multi - hop approach is similar to most existing approaches . However , by using a MLP that can effectively deal with complex relatedness when searching for the right supporting sentences among a lot of sentences , RMN raised the state - of - the - art performance on the story - based QA and goal - oriented dialog dataset . When comparing RN which also used MLP to understand relations , RMN was more effective in the case of large memory . A Statistical Re-weighting Method . Sequence-to-sequence neural generation models have achieved promising performance on short text conversation tasks . However , they tend to generate generic/dull responses , leading to unsatisfying dialogue experience . Because existing online conference system do n't have function for analysis of remote sensing data . This system has functions of displaying common image data on all clients , simple data processing on the displayed image , marking with vector graphics on the display , and a text-based conversation . All clients ' displays of the conference are controlled and synchronized by a server system . The main contents and evaluation methods are then analyzed , an integrated research framework is proposed , and the key technologies of framework are analyzed . Finally , the key issues , difficulties , and future directions in the field of text conversation are all summarized . As an important step of human-computer interaction , conversion generation has attracted much attention and has a rising tendency in recent years . This paper gives a detailed description about an ensemble system for short text conversation generation . The proposed system consists of four subsystems , a quick response candidates selecting module , an information retrieval system , a generation-based system and an ensemble module . An Investigation of Human-Human Text Conversation with AI Support . Despite a growing body of research about the design and use of conversational agents , existing work has almost exclusively focused on interactions between an agent and a human . Experimental results have shown that SLRTM outperforms several strong baselines on various tasks . Furthermore , SLRTM can automatically generate sentences given a topic ( i.e. , topics to sentences ) , which is a key technology for real world applications such as personalized short text conversation . The focus in this paper is to consider the effects of lying and truth-telling on the human judges by the hidden entities , whether human or a machine . Turing test transcripts provide a glimpse into short text communication , the type that occurs in emails : how does the reader determine truth from the content of a stranger ’s textual message ? Different types of lying in the conversations are explored , and the judge ’s attribution of human or machine is investigated in each test . In addition , we also discuss different chatbot platforms and development frameworks of recent times . Our core emphasis is on analysis of recent development approaches of textbased conversational systems . We identify few challenges in intelligent chatbot development that may be helpful for future research works .
A well modeled discourse is the fundamental work of building a dialogue system within a problem domain . The detail of the modeling process could be different from domain to domain .
AUTHOR Bardovi-Harlig , Kathleen ; Hartford , Beverly S. TITLE Refining the DCT : Comparing Open Questionnaires and Dialogue Completion Tasks . PUB DATE 93 NOTE 25p .
Using autoencoders allows handling a large variety of data , such as images , text or even dialogues . Our experiments show the flexibility of the proposed approach in dealing with different types of data in different settings : images with CIFAR-10 and CIFAR-100 ( not-in-training setting ) , text with Amazon reviews ( PU learning ) and dialogues with Facebook bAbI ( next response classification and dialogue completion ) .
However , it heavily relies on the distribution of entities across adjacent sentences but ignores the emotional context embedded in non-entity text and fails to model long dependencies between speech intentions . These limitations become even more severe when applied to dialogue domain since sentences in dialogue are short , informal and colloquial , thereby , less entities could be extracted and less coherence information could be expressed in these grids . To address the limitations of entity gird methods and incorporate the structure knowledge of dialogue , we propose a new neural network architecture , Hierarchical Intention Enhance Network , to integrate semantic context and speech intention in both utterance and dialogue levels to hierarchically model the global coherence without any entity grids .
In recent years , dialogue optimization using reinforcement learning has evolved to be a state-of-the-art technique . The primary focus of research in the dialogue domain is to learn some optimal policy with regard to the task description ( reward function ) and the user simulation being employed . However , in case of human-human interaction , the parties involved in the dialogue conversation mutually evolve over the period of interaction .
In particular , we present a formalisation of the dialogue gameboard introduced by Jonathan Ginzburg . From a more general point of view , we show that DL is particularly well suited to develop rigorous formal foundations for an approach to dialogue dynamics based on information state updates .
Conversational agents offer users a naturallanguage interface to accomplish tasks , entertain themselves , or access information . Informational dialogue is particularly challenging in that the agent has to hold a conversation on an open topic , and to achieve a reasonable coverage it generally needs to digest and present unstructured information from textual sources . Making responses based on such sources sound natural and fit appropriately into the conversation context is a topic of ongoing research , one of the key issues of which is preventing the agent ’s responses from sounding repetitive .
Conversational telephone speech ( CTS ) collections of Arabic dialects distributed trough the Linguistic Data Consortium ( LDC ) provide an invaluable resource for the development of robust speech systems including speaker and speech recognition , translation , spoken dialogue modeling , and information summarization . They are frequently relied on also in language ( LID ) and dialect identification ( DID ) evaluations .
Both the linear chain CRF model and the LSTM-CRF model outperform the naı̈ve baselines by a large margin , and LSTM-CRF has an edge between the two . To our knowledge , this is the first study on dialogue segment annotation using neural network models . This study is also a stepping-stone to automating the microgenetic analysis of cognitive interactions between students .
However , the conversational complexities demand building more personalized and user-oriented dialogue process systems . To support human-computer dialogue process many models have been proposed . Considering personalization aspect , this research work presents novel Context-aware Dialogue Manager ( CADM ) model with its foundation based on well-known JDL fusion model .
In this paper , we propose a novel hierarchical variational memory network ( HVMN ) , by adding the hierarchical structure and the variational memory network into a neural encoder-decoder network . By emulating human-to-human dialogues , our proposed method can capture both the high-level abstract variations and long-term memories during dialogue tracking , which enables the random access of relevant dialogue histories . Extensive experiments conducted on three large real-world datasets verify a significant improvement of our proposed model against state-of-the-art baselines for dialogue generation .
For establishing communications and modeling a framework for dialogue between intelligent agents , it is beneficial to have a well-defined strategy . In a dialogue run , the strategies are the manners to achieve the agents ’ goals by selecting the best option between the different existent choices . There are not many works which define the strategies for dialogue between agents .
While most works approach it as a simple categorization problem , sentiment analysis is actually a suitcase research problem that requires tackling many NLP tasks , including word polarity disambiguation , subjectivity detection , personality recognition , microtext normalization , concept extraction , time tagging , and aspect extraction . Sentiment analysis has raised growing interest both within the scientific community , leading to many exciting open challenges , as well as in the business world , due to the remarkable benefits to be had from financial and political forecasting , e - health and e - tourism , user profiling and community detection , manufacturing and supply chain applications , human communication comprehension and dialogue systems , etc . In the field of emotion recognition , early works by and showed that fusion of audio and visual systems , creating a bimodal signal , yielded a higher accuracy than any unimodal system .
Neural network language models have played a central role in recent natural language processing ( NLP ) advances . For example , neural encoder - decoder models , which were successfully applied to various natural language generation tasks including machine translation , summarization , and dialogue , can be interpreted as conditional neural language models . Neural language models also positively influence syntactic parsing .
Sequence generation and prediction tasks span many modes of data , ranging from audio and language modelling , to more general timeseries prediction tasks . Applications of such models include speech recognition , machine translation , dialogue generation , speech synthesis , forecasting , and music generation , among others . Neural networks can be applied to these tasks by predicting sequence elements one - by - one , conditioning on the history of sequence elements , forming an autoregressive model .
Sequential recurrent neural networks ( RNNs ) are remarkably effective models of natural language . In the last few years , language model results that substantially improve over long - established state - of - the - art baselines have been obtained using RNNs as well as in various conditional language modeling tasks such as machine translation , image caption generation , and dialogue generation . Despite these impressive results , sequential models are a priori inappropriate models of natural language , since relationships among words are largely organized in terms of latent nested structures rather than sequential surface order .
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 . The dialogue system then uses this estimated state to plan the next action and respond to the user .
We show that by doing so , GLAD generalizes on rare slot - value pairs with few training examples . GLAD achieves state - of - the - art results of 88.1 % goal accuracy and 97.1 % request accuracy on the WoZ dialogue state tracking task wen2017NetworkBasedEndToEndDialogueSystem , outperforming prior best by 3.7 % and 5.5 % . On DSTC2 dstc2 , we achieve 74.5 % goal accuracy and 97.5 % request accuracy , outperforming prior best by 1.1 % and 1.0 % .
subsection : Dataset The Dialogue Systems Technology Challenges ( DSTC ) provides a common framework for developing and evaluating dialogue systems and dialogue state trackers . Under this framework , dialogue semantics such as states and actions are based on a task ontology such as restaurant reservation .
section : Related Work Dialogue State Tracking . Traditional dialogue state trackers rely on a separate SLU component that serves as the initial stage in the dialogue agent pipeline . The downstream tracker then combines the semantics extracted by the SLU with previous dialogue context in order to estimate the current dialogue state . Recent results in dialogue state tracking show that it is beneficial to jointly learn speech understanding and dialogue tracking . These approaches directly take as input the N - best list produced by the ASR system .
In recent years , several models have been proposed to integrate the language and vision modalities ; usually the integration is operationalized by element - wise multiplication between linguistic and visual vectors . Though the interest in these modalities has spread in an astonishing way thanks to various multimodal tasks proposed , including the IC , VQA , Visual Reasoning and Visual Dialogue tasks mentioned above , very little work has been done on grounding entailment . Interestingly , youn :
We show that our method leads to improved performance on both a synthetic task , and for German - English machine translation . Our analysis paves the way for such methods to be applied in natural language generation tasks , such as machine translation , caption generation , and dialogue modelling . section : Introduction
This work serves our larger goal of identifying patterns of tutors ’ actions , in the form of dialogue acts , that relate to learning . The preliminary results we obtained for dialogue act classification using a machine learning approach are promising .
This paper presents an unsupervised dialogue act modeling approach that leverages the influence of learner characteristics , particularly students ’ perceptions of their own skill , on their language use . The experimental findings show that leveraging skill perception within dialogue act classification improves performance of the models , producing better accuracy . This line of investigation will inform the design of nextgeneration tutorial dialogue systems , which leverage machinelearned models to adapt to their users .
This paper describes an approach in which phrase accent information is used for dialogue act recogniti on in German spontaneous speech This application is an example of how automatically computed pros odic information can be used in automatic speech re cognition Usually the important intention conveyed by an utterance is found in the focused area which is often accentuated When all the words of an ut terance are used for dialogue act classi cation the best result is achieved only if all probabilities e g of n grams are known In real life applications this not the case Because utterances can be very similar to one another but belong to di erent dialogue act classes it may be possible to distinguish the classes on the basis of characteristic words For this reason dialogue act classi cation is often based on keyword detection The selection of keywords is crucial Bet ter recognition relies on better chosen keywords This paper shows how keyword selection can be improved by using two additional information sources lexical POS information and prosody POS and prosodic in formation is used to build subsets of the vocabulary to improve recognition Experiments are conducted on a sub sample of the VERBMOBIL corpus The aim is to distinguish between four dialogue act sub classes of the general class SUGGEST
The emergency type should be decided with insufficient information and the decided type leads to the next information . In this paper , we propose a model of dialogue classification to determine the objective of the dialogue while the conversation is on its way . Situation of the conversation is used for decision making .
Finally , we propose a novel study that evaluates the impact of the Word Error Rate ( WER ) in the LDA topic space learning process as well as during the theme identification task . This original qualitative study points out that selecting a small subset of words having the lowest WER ( instead of using all the words ) allows the system to better classify automatic transcriptions with an absolute gain of 0.9 point , in comparison to the best performance achieved on this dialogue classification task ( precision of 83.3 % ) .
We look at the effect of using high level discourse knowledge in dialogue act type detection . We also look at ways this knowledge can be used for improving language modelling and intonation modelling of utterance types .
While this is easy for humans , the present neural methods that rely on learned word embeddings may not perform well for these NLP tasks , especially in the presence of Out-Of-Vocabulary ( OOV ) or rare NEs . In this paper , we propose a solution for this problem , and present empirical evaluations on : a ) a structured Question-Answering task , b ) three related Goal-Oriented dialog tasks , and c ) a Reading-Comprehension task , which show that the proposed method can be effective in dealing with both in-vocabulary and OOV NEs . We create extended versions of dialog bAbI tasks 1,2 and 4 and OOV versions of the CBT test set available at - https://github.com/IBM/ne-table-datasets .
The questioner figures out the answerer ’s intention 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 dialog tasks : MNIST Counting Dialog ' ' and GuessWhat?!. ' ' In our experiments , AQM outperforms comparative algorithms by a large margin .
However , these models require a large corpus of dialogues to learn effectively . For goal-oriented dialogues , such datasets are expensive to collect and annotate , since each task involves a separate schema and database of entities . Further , the Wizard-of-Oz approach commonly used for dialogue collection does not provide sufficient coverage of salient dialogue flows , which is critical for guaranteeing an acceptable task completion rate in consumer-facing conversational agents .
Developing agents to engage in complex goal-oriented dialogues is challenging partly because the main learning signals are very sparse in long conversations . In this paper , we propose a divide-and-conquer approach that discovers and exploits the hidden structure of the task to enable efficient policy learning .
We demonstrate the genericity of wide domain models , such as broadcast news acoustic and language models , and techniques to achieve a higher degree of genericity , such as transparent methods to adapt such models to a specific task . This work targets three tasks using commonly available corpora : small vocabulary recognition ( TI-digits ) , text dictation ( WSJ ) , and goal-oriented spoken dialog ( ATIS ) .
Since then , the various contributors have obviously worked at adding tutorial and review material to the chapters to supplement the research reports . This is very useful , especially since the primary effect of the book will be to promote the idea that interactive software systems can eventually become intelligent partners in goal-directed dialogs with humans . The book is by no means an engineering handbook ; it is primarily theoretical , but includes sketches of requirements specifications and feasibility demonstrations .
They learn with the assumption that at any time there is only one correct next utterance . In this work , we focus on this problem in the goal-oriented dialog setting where there are different paths to reach a goal . We propose a new method , that uses a combination of supervised learning and reinforcement learning approaches to address this issue .
The task of Next Utterance Classification in dialog learning highly resembles that of Question Answering , but there has not been much attention to applying models across the two fields , especially not in more practical dialog modeling scenarios . Hence , this paper presents end-to-end Question Answering ( QA ) style models for the goal-oriented dialog system task in Dialog System Technology Challenges ( DSTC ) 7 . We first provide a comprehensive quantitative and qualitative analysis of the newly introduced Advising dataset and show the heavy reliance of the data on an external Knowledge Base ( KB ) of course offerings .
The noetic end-to-end response selection challenge as one track in Dialog System Technology Challenges 7 ( DSTC7 ) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems , for which participants need to select the correct next utterances from a set of candidates for the multi-turn context . This paper describes our systems that are ranked the top on both datasets under this challenge , one focused and small ( Advising ) and the other more diverse and large ( Ubuntu ) .
Vast improvements in natural language understanding and speech recognition have paved the way for conversational interaction with computers . While conversational agents have often been used for short goal-oriented dialog , we know little about agents for developing computer programs . To explore the utility of natural language for programming , we conducted a study ( $ n$=45 ) comparing different input methods to a conversational programming system we developed .
Multiple access to the external memory to find out necessary information through a multi - hop approach is similar to most existing approaches . However , by using a MLP that can effectively deal with complex relatedness when searching for the right supporting sentences among a lot of sentences , RMN raised the state - of - the - art performance on the story - based QA and goal - oriented dialog dataset . When comparing RN which also used MLP to understand relations , RMN was more effective in the case of large memory .
A Statistical Re-weighting Method . Sequence-to-sequence neural generation models have achieved promising performance on short text conversation tasks . However , they tend to generate generic/dull responses , leading to unsatisfying dialogue experience .
Because existing online conference system do n't have function for analysis of remote sensing data . This system has functions of displaying common image data on all clients , simple data processing on the displayed image , marking with vector graphics on the display , and a text-based conversation . All clients ' displays of the conference are controlled and synchronized by a server system .
The main contents and evaluation methods are then analyzed , an integrated research framework is proposed , and the key technologies of framework are analyzed . Finally , the key issues , difficulties , and future directions in the field of text conversation are all summarized .
As an important step of human-computer interaction , conversion generation has attracted much attention and has a rising tendency in recent years . This paper gives a detailed description about an ensemble system for short text conversation generation . The proposed system consists of four subsystems , a quick response candidates selecting module , an information retrieval system , a generation-based system and an ensemble module .
An Investigation of Human-Human Text Conversation with AI Support . Despite a growing body of research about the design and use of conversational agents , existing work has almost exclusively focused on interactions between an agent and a human .
Experimental results have shown that SLRTM outperforms several strong baselines on various tasks . Furthermore , SLRTM can automatically generate sentences given a topic ( i.e. , topics to sentences ) , which is a key technology for real world applications such as personalized short text conversation .
The focus in this paper is to consider the effects of lying and truth-telling on the human judges by the hidden entities , whether human or a machine . Turing test transcripts provide a glimpse into short text communication , the type that occurs in emails : how does the reader determine truth from the content of a stranger ’s textual message ? Different types of lying in the conversations are explored , and the judge ’s attribution of human or machine is investigated in each test .
In addition , we also discuss different chatbot platforms and development frameworks of recent times . Our core emphasis is on analysis of recent development approaches of textbased conversational systems . We identify few challenges in intelligent chatbot development that may be helpful for future research works .
A Conclusive Life Cycle of Discourse Modeling Refining the DCT: Comparing Open Questionnaires and Dialogue Completion Tasks. Reconstruct & Crush Network Hierarchical Intention Enhanced Network for Automatic Dialogue Coherence Assessment Co-adaptation in Spoken Dialogue Systems A Dynamic Logic Formalisation of the Dialogue Gameboard Redundancy Localization for the Conversationalization of Unstructured Responses Arabic Dialect Identification - 'Is the Secret in the Silence?' and Other Observations Automatic Labeling of Problem-Solving Dialogues for Computational Microgenetic Learning Analytics Intent-Context Fusioning in Healthcare Dialogue-Based Systems Using JDL Model Hierarchical Variational Memory Network for Dialogue Generation A new dialogue strategy in multi-agent systems Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling Direct Output Connection for a High-Rank Language Model Dynamic Evaluation of Neural Sequence Models Recurrent Neural Network Grammars Global-Locally Self-Attentive Dialogue State Tracker Grounded Textual Entailment An Actor-Critic Algorithm for Sequence Prediction Dialogue Act Classification In Human-to-Human Tutorial Dialogues A Preliminary Investigation of Learner Characteristics for Unsupervised Dialogue Act Classification Using Phrase Accent Information for Dialogue Act Recognition in Spontaneous German Speech A Situation-Based Dialogue Classification Model for Emergency Calls Impact of Word Error Rate on theme identification task of highly imperfect human-human conversations Using high level dialogue information for dialogue act recognition using prosodic features. NE-Table: A Neural key-value table for Named Entities Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning Subgoal Discovery for Hierarchical Dialogue Policy Learning Towards task-independent speech recognition Book review: User Models in Dialog Systems Edited by A. Kobsa and W. Wahlster (Springer-Verlag, 1989) Learning End-to-End Goal-Oriented Dialog with Multiple Answers End-to-End Question Answering Models for Goal-Oriented Dialog Learning Sequential Attention-based Network for Noetic End-to-End Response Selection Convo: What does conversational programming need? An exploration of machine learning interface design Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method Online conference system for collaborative work to make thematic maps Research of Dynamic Text Conversation Extraction An Ensemble Approach to Conversation Generation AI-Supported Messaging: An Investigation of Human-Human Text Conversation with AI Support Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves Effects of lying in practical Turing tests Survey of Textbased Chatbot in Perspective of Recent Technologies
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IC-AI NIPS IJCNN Natural Interaction with Robots, Knowbots and Smartphones, Putting Spoken Dialog Systems into Practice EACL SIGDIAL Conference INTERSPEECH LREC ICOST WWW Knowl. Based Syst. EMNLP ICML HLT-NAACL ACL COLING ICLR EDM NeurIPS NAACL-HLT (3) ICASSP EMNLP ArXiv EMNLP CIT NLPCC CHI Extended Abstracts CICBA (2)
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