Many challenges in natural language processing require generating text , including language translation , dialogue generation , and speech recognition . For all of these problems , text generation becomes more difficult as the text becomes longer . As the quick development in neural networks , the sequence to sequence ( seq2seq ) model which employed recurrent neural networks ( RNN ) encoder-decoder has archived great success in machine translation . Many researchers began to apply this model in dialogue response generation . However , the conventional seq2seq model counters several problems , e.g. , grammatical mistake , safe response and etc . An Application to Dialogue Response Generation . We introduce the multiresolution recurrent neural network , which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes : a sequence of high-level coarse tokens , and a sequence of natural language tokens . Although no experimental results are available to support our proposition , the preliminary analysis of politeness maxims presents a promising future , as guidelines for the synthesis of polite robot dialogue . Through effective dialogue creation , the interaction with human and robots will be more pleasant , indicating a step forward in effective human-robot interaction . These design principles must be made readily available to help programmers in constructing dialogues , so the authors are constructing a tool , an expert system that contains knowledge about behavioral studies gleaned from human factors experimenters . Dialogue creation encompasses a very large area , so the domain of expertise has initially been restricted to the creation of menu displays . This prototype expert system helps programmers creating menus by giving advice on the proper formatting of the menu . We will discuss an approach to dialogue act generation that reflects the multidimensionality of communication . Dialogue acts from different dimensions in the taxonomy used are generated in parallel , resulting in a buffer of candidates . We present a demo of our conversational system POLLy ( POliteness in Language Learning ) which uses a common planning representation to generate actions to be performed by embodied agents in a virtual environment and to generate spoken utterances for dialogues about the steps involved in completing the task . In order to generate socially appropriate dialogue , Brown and Levinson ’s theory of politeness is used to constrain the dialogue generation process . The present article deals with the position of spoken language in Hebrew narrative fiction and drama , and the modes of its representation from 1948 on , including both original Hebrew works and those translated from English into Hebrew . This issue is discussed against the background of such relevant broader issues as : the special situation of Hebrew , which had long been used as a written language only , devoid of the varied functions of spoken language ; linguistic-stylistic norms in Hebrew literature from 1948 on and the changes they underwent ; Hebrew writers ' and translators ' awareness of the principles of spoken language in general , and those of the Hebrew vernacular in particular ; differences in dialogue formation between various literary sub-systems : drama as distinct from narrative fiction and original literature as distinct from translated literature , including some cross-sections of both . The issues are discussed from both the synchronic and diachronic points of view . Explanatory " is an apt term to describe this book by Alison Cawsey . Her style is perfectly suited to the needs of both experts and novices in the dialogue generation area . Step by step , she leads us through the foremost trends and theories dealing with the development of computer systems for the explanation of pieces of knowledge , examining their advantages , disadvantages and possible applications , while introducing us to the approaches she has taken in the construction of her own system for the generation of explanatory discourse . This model can be utilized as a tree-structured decoder in the framework of " X to tree " learning , where X stands for any structure ( e.g. chain , tree etc . ) that can be represented as a latent vector . By transforming the dialogue generation problem into a sequence-to-tree task , we demonstrate the proposed X2Tree framework achieves a 11.15 % increase of response acceptance ratio over the baseline methods . Given this population , the assignments I have found most successful in teaching Introduction to NLP involve concrete projects where students could see for themselves the phenomena discussed in class . This paper describes three of my most successful assignments : duplicating Kernighan et al. 's Bayesian approach to spelling correction , a study of Greenberg 's universals in the student 's native language , and a dialogue generation project . For each assignment I discuss what the students learned and why the assignment was successful . After investigating the currently used models , we found that one primary challenge is to model and generate informative words , such as named entities , especially when the entities have sparsely existed in training corpus . To address this problem , we propose to augment neural network-based generative architecture with knowledge embedding and knowledge attentive reader to incorporate external textual knowledge into the dialogue model to facilitate the dialogue modeling and generation . We evaluate the model with the Ubuntu dataset through automatic evaluation metrics and human evaluation . Sequence - to - Sequence learning with deep neural networks ( herein , seq2seq ) has rapidly become a very useful and surprisingly general - purpose tool for natural language processing . In addition to demonstrating impressive results for machine translation , roughly the same model and training have also proven to be useful for sentence compression , parsing , and dialogue systems , and they additionally underlie other text generation applications , such as image or video captioning . The dominant approach to training a seq2seq system is as a conditional language model , with training maximizing the likelihood of each successive target word conditioned on the input sequence and the gold history of target words . In particular , in spite of the importance to dialogue translation of the degree of ambiguity in the sentence-final expressions that indicate the speaker 's intention and colloquial fragmentary expressions that are characteristic of spoken language , conventional techniques do not address this problem . This paper describes first a method for dialogue understanding by means of a layered plan recognition method which uses knowledge on the conduct of a dialogue as well as knowledge concerning the topic , and the use of stacks to manage the states of understand-ing that this method produces . Then a method is presented for obtaining abstract contextual information concerning the next utterance by referring to the stack contents . Using a posttest only experimental design students were divided into a group that used a standard discussion board and one that used an annotation or ‘ anchored discussion ’ system . The used research instruments were : dialogue analysis , questionnaires and focus group interviews . Results will be analyzed and discussed in the light of the difference between the 2 conditions . A student-computer dialogue is presented , which illustrates how some of these strategies are implemented in a modified form in SCHOLAR . The analytical method presented here could be extended to a wide range of conversational situations and this method ( Dialogue Analysis ) would permit psychologists to study questions related to interactive aspects of human language processing that can not even be considered with traditional laboratory methods . This chapter focuses on the teaching of communication and interaction skills to learners who have already had the chance to acquire the basics of dialogue interpreting ( DI ) and to practice it through role-playing . It argues that traditional simulated scenarios should be complemented by alternative techniques using authentic data , and research findings about them , and suggests how this can be done with students at undergraduate and especially graduate level , as they learn to adapt their skills to particular interactional contingencies and to make judgments about particular situations . Dialogue interpreting is a familiar form of interpreting that happened most frequently in our daily interpreted activities . In the dialogue interpreting , the two sides are maintaining a high level of interaction and are responsive to each other . Dialogue Act Recognition ( DAR ) is a challenging problem in dialogue interpretation , which aims to associate semantic labels to utterances and characterize the speaker 's intention . Currently , many existing approaches formulate the DAR problem ranging from multi-classification to structured prediction , which suffer from handcrafted feature extensions and attentive contextual dependencies . In Dynamic Syntax , both parsing and generation are defined in terms of actions on semantic tree structures , allowing these structures to be built in a word-by-word incremental fashion . This paper proposes a model of dialogue context which includes these trees and their associated actions , and shows how alignment and routinization result directly from minimisation of lexicon search ( and hence speaker ’s effort ) , and how switch of speaker/hearer roles in shared utterances can be seen as a switch between incremental processes directed by different goals , but sharing the same ( partial ) data structures . In DS , word-by-word incremental parsing and generation are defined in terms of actions on semantic tree structures . This paper proposes a model of dialogue context which includes these trees and their associated actions , and shows how alignment and routinization result directly from minimisation of lexicon search ( and hence speaker 's effort ) , and how switch of speaker/hearer roles in shared utterances can be seen as a switch between incremental processes directed by different goals , but sharing the same ( partial ) data structures . Article Addressee responses as cues to the addressee ’s stance alone did not bias overhearer recall . These findings support the argument that perception of a shared reality is a central component of dialogue comprehension . Text segmentation task is an essential processing task for many of Natural Language Processing ( NLP ) such as text summarization , text translation , dialogue language understanding , among others . Turns segmentation considered the key player in dialogue understanding task for building automatic Human-Computer systems . In addition , based on this hypothesis , we propose a new method for fine-tuning multilingual BERT in downstream cross-lingual sequence labeling tasks . Experimental results on dialogue natural language understanding , part-of-speech tagging , and named entity recognition tasks show that reducing word order information fitted to the model can achieve better zero-shot cross-lingual performance . Furthermore , our proposed methods can also be applied to strong cross-lingual baselines , and improve their performances . Most of these examples are taken from our own work in the V erbmobil speech-to-speech translation system and in the EVAR train timetable dialogue system . In a ` prosodic orbit ' , we first present units , phenomena , annotations and statistical methods from the signal ( acoustics ) to the dialogue understanding phase . We show then , how prosody can be used together with other knowledge sources for the task of resegmentation if a first segmentation turns out to be wrong , and how an integrated approach leads to better results than a sequential use of the different knowledge sources ; then we present a hybrid approach which is used to perform a shallow parsing and which uses prosody to guide the parsing ; finally , we show how a critical system evaluation can help to improve the overall performance of automatic dialogue systems . Spoken language understanding ( SLU ) is a key requirement of spoken dialogue systems ( SDS ) . The role of SLU parser is to robustly interpret the meanings of users ’ utterance using a hand-crafted grammar that is expensive to build . Dialogue inferencing is the knowledge-intensive process of inferring aspects of a user 's problem from its partial description . Conversational case-based reasoning ( CCBR ) systems , which interactively and incrementally elicit a user 's problem description , suffer from poor retrieval efficiency ( i.e. , they prompt the user with questions that the user has already implicitly answered ) unless they perform dialogue inferencing . The standard method for dialogue inferencing in CCBR systems requires library designers to supply explicit inferencing rules . Due to their simple implementation of CBR technology , these tools were almost ignored by the research community ( until recently ) , even though their use introduced many interesting applied research issues . We detail our progress on addressing three of these issues : simplifying case authoring , dialogue inferencing , and interactive planning . We describe evaluations of our approaches on these issues in the context of NaCoDAE and HICAP , our CCBR tools . It presents a novel model for automatic generation of dialogue systems built from contents . This approach is similar to and relies on a search engine , but with augmented dialogue capabilities : at each dialogue turn , the system propose n keywords , in order to optimise the information gain expectation . However , the performance is not satisfactory : the neural network tends to generate safe , universally relevant replies which carry little meaning . In this paper , we propose a content-introducing approach to neural network-based generative dialogue systems . We first use pointwise mutual information ( PMI ) to predict a noun as a keyword , reflecting the main gist of the reply . By comparing the dialogues under two conditions , it seems that there are two types of head movements , one is intended to give a response to his partner and the other is to send some signal . We analyze how the visual information contributes in spoken dialogue perception , and the possibility of adopting it in a multi-modal human interface . If argumentation-based dialogues are considered as communicative games between two or more agents , speech acts can be viewed as their building blocks . They are used to compose complex dialogues , such as persuasion , deliberation , information seeking , negotiation or inquiry and can be seen as the underlying reactive layer of communication ( see [ 35 ] for the definitions of various dialogue types , and [ 1,4,10,14,16,17,29,30,32 ] for investigations in multi-agent argumentation-based dialogue ) . In the process of exchanging messages , we naturally treat the sender and the receiver as two independent information sources , which try to expand , update , and revise their beliefs through communication . We show how Bayesian networks and related probabilistic methods provide an efficient way of capturing the complex balancing of different factors that determine interpretation and generation in dialogue . As a case study , we show how a probabilistic approach can be used to model anaphora resolution in dialogue . This research was Classroom Action Research . This research was aimed for ( 1 ) improving the quality of teaching in writing dialogue ability in unggah-ungguh basa and ( 2 ) improving the result quality of writing dialogue ability inunggah-ungguh basa . The Classroom Action Research was done in SMP Negeri 2 Kebakkramat . This paper focuses on the design and evaluation of SARA , a conversational agent for the touristic domain featuring a high number of different , unique characteristics : spoken dialogue interaction , dialogue orchestration , context dependent information , an animated avatar and support for different kind of dialogue types , i.e. chat , specific and general question-answering , task oriented dialogues . The agent has currently two implementations : as web client and as mobile phone application . Methods for domain independent dialogue systems have been proposed in previous research , however each of them has its own limitations . This paper presents a new approach , a task ontology model for domain independent dialogue management . An abstract task ontology is developed and based on that a generic dialogue manager is created .
Many challenges in natural language processing require generating text , including language translation , dialogue generation , and speech recognition . For all of these problems , text generation becomes more difficult as the text becomes longer .
As the quick development in neural networks , the sequence to sequence ( seq2seq ) model which employed recurrent neural networks ( RNN ) encoder-decoder has archived great success in machine translation . Many researchers began to apply this model in dialogue response generation . However , the conventional seq2seq model counters several problems , e.g. , grammatical mistake , safe response and etc .
An Application to Dialogue Response Generation . We introduce the multiresolution recurrent neural network , which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes : a sequence of high-level coarse tokens , and a sequence of natural language tokens .
Although no experimental results are available to support our proposition , the preliminary analysis of politeness maxims presents a promising future , as guidelines for the synthesis of polite robot dialogue . Through effective dialogue creation , the interaction with human and robots will be more pleasant , indicating a step forward in effective human-robot interaction .
These design principles must be made readily available to help programmers in constructing dialogues , so the authors are constructing a tool , an expert system that contains knowledge about behavioral studies gleaned from human factors experimenters . Dialogue creation encompasses a very large area , so the domain of expertise has initially been restricted to the creation of menu displays . This prototype expert system helps programmers creating menus by giving advice on the proper formatting of the menu .
We will discuss an approach to dialogue act generation that reflects the multidimensionality of communication . Dialogue acts from different dimensions in the taxonomy used are generated in parallel , resulting in a buffer of candidates .
We present a demo of our conversational system POLLy ( POliteness in Language Learning ) which uses a common planning representation to generate actions to be performed by embodied agents in a virtual environment and to generate spoken utterances for dialogues about the steps involved in completing the task . In order to generate socially appropriate dialogue , Brown and Levinson ’s theory of politeness is used to constrain the dialogue generation process .
The present article deals with the position of spoken language in Hebrew narrative fiction and drama , and the modes of its representation from 1948 on , including both original Hebrew works and those translated from English into Hebrew . This issue is discussed against the background of such relevant broader issues as : the special situation of Hebrew , which had long been used as a written language only , devoid of the varied functions of spoken language ; linguistic-stylistic norms in Hebrew literature from 1948 on and the changes they underwent ; Hebrew writers ' and translators ' awareness of the principles of spoken language in general , and those of the Hebrew vernacular in particular ; differences in dialogue formation between various literary sub-systems : drama as distinct from narrative fiction and original literature as distinct from translated literature , including some cross-sections of both . The issues are discussed from both the synchronic and diachronic points of view .
Explanatory " is an apt term to describe this book by Alison Cawsey . Her style is perfectly suited to the needs of both experts and novices in the dialogue generation area . Step by step , she leads us through the foremost trends and theories dealing with the development of computer systems for the explanation of pieces of knowledge , examining their advantages , disadvantages and possible applications , while introducing us to the approaches she has taken in the construction of her own system for the generation of explanatory discourse .
This model can be utilized as a tree-structured decoder in the framework of " X to tree " learning , where X stands for any structure ( e.g. chain , tree etc . ) that can be represented as a latent vector . By transforming the dialogue generation problem into a sequence-to-tree task , we demonstrate the proposed X2Tree framework achieves a 11.15 % increase of response acceptance ratio over the baseline methods .
Given this population , the assignments I have found most successful in teaching Introduction to NLP involve concrete projects where students could see for themselves the phenomena discussed in class . This paper describes three of my most successful assignments : duplicating Kernighan et al. 's Bayesian approach to spelling correction , a study of Greenberg 's universals in the student 's native language , and a dialogue generation project . For each assignment I discuss what the students learned and why the assignment was successful .
After investigating the currently used models , we found that one primary challenge is to model and generate informative words , such as named entities , especially when the entities have sparsely existed in training corpus . To address this problem , we propose to augment neural network-based generative architecture with knowledge embedding and knowledge attentive reader to incorporate external textual knowledge into the dialogue model to facilitate the dialogue modeling and generation . We evaluate the model with the Ubuntu dataset through automatic evaluation metrics and human evaluation .
Sequence - to - Sequence learning with deep neural networks ( herein , seq2seq ) has rapidly become a very useful and surprisingly general - purpose tool for natural language processing . In addition to demonstrating impressive results for machine translation , roughly the same model and training have also proven to be useful for sentence compression , parsing , and dialogue systems , and they additionally underlie other text generation applications , such as image or video captioning . The dominant approach to training a seq2seq system is as a conditional language model , with training maximizing the likelihood of each successive target word conditioned on the input sequence and the gold history of target words .
In particular , in spite of the importance to dialogue translation of the degree of ambiguity in the sentence-final expressions that indicate the speaker 's intention and colloquial fragmentary expressions that are characteristic of spoken language , conventional techniques do not address this problem . This paper describes first a method for dialogue understanding by means of a layered plan recognition method which uses knowledge on the conduct of a dialogue as well as knowledge concerning the topic , and the use of stacks to manage the states of understand-ing that this method produces . Then a method is presented for obtaining abstract contextual information concerning the next utterance by referring to the stack contents .
Using a posttest only experimental design students were divided into a group that used a standard discussion board and one that used an annotation or ‘ anchored discussion ’ system . The used research instruments were : dialogue analysis , questionnaires and focus group interviews . Results will be analyzed and discussed in the light of the difference between the 2 conditions .
A student-computer dialogue is presented , which illustrates how some of these strategies are implemented in a modified form in SCHOLAR . The analytical method presented here could be extended to a wide range of conversational situations and this method ( Dialogue Analysis ) would permit psychologists to study questions related to interactive aspects of human language processing that can not even be considered with traditional laboratory methods .
This chapter focuses on the teaching of communication and interaction skills to learners who have already had the chance to acquire the basics of dialogue interpreting ( DI ) and to practice it through role-playing . It argues that traditional simulated scenarios should be complemented by alternative techniques using authentic data , and research findings about them , and suggests how this can be done with students at undergraduate and especially graduate level , as they learn to adapt their skills to particular interactional contingencies and to make judgments about particular situations .
Dialogue interpreting is a familiar form of interpreting that happened most frequently in our daily interpreted activities . In the dialogue interpreting , the two sides are maintaining a high level of interaction and are responsive to each other .
Dialogue Act Recognition ( DAR ) is a challenging problem in dialogue interpretation , which aims to associate semantic labels to utterances and characterize the speaker 's intention . Currently , many existing approaches formulate the DAR problem ranging from multi-classification to structured prediction , which suffer from handcrafted feature extensions and attentive contextual dependencies .
In Dynamic Syntax , both parsing and generation are defined in terms of actions on semantic tree structures , allowing these structures to be built in a word-by-word incremental fashion . This paper proposes a model of dialogue context which includes these trees and their associated actions , and shows how alignment and routinization result directly from minimisation of lexicon search ( and hence speaker ’s effort ) , and how switch of speaker/hearer roles in shared utterances can be seen as a switch between incremental processes directed by different goals , but sharing the same ( partial ) data structures .
In DS , word-by-word incremental parsing and generation are defined in terms of actions on semantic tree structures . This paper proposes a model of dialogue context which includes these trees and their associated actions , and shows how alignment and routinization result directly from minimisation of lexicon search ( and hence speaker 's effort ) , and how switch of speaker/hearer roles in shared utterances can be seen as a switch between incremental processes directed by different goals , but sharing the same ( partial ) data structures . Article
Addressee responses as cues to the addressee ’s stance alone did not bias overhearer recall . These findings support the argument that perception of a shared reality is a central component of dialogue comprehension .
Text segmentation task is an essential processing task for many of Natural Language Processing ( NLP ) such as text summarization , text translation , dialogue language understanding , among others . Turns segmentation considered the key player in dialogue understanding task for building automatic Human-Computer systems .
In addition , based on this hypothesis , we propose a new method for fine-tuning multilingual BERT in downstream cross-lingual sequence labeling tasks . Experimental results on dialogue natural language understanding , part-of-speech tagging , and named entity recognition tasks show that reducing word order information fitted to the model can achieve better zero-shot cross-lingual performance . Furthermore , our proposed methods can also be applied to strong cross-lingual baselines , and improve their performances .
Most of these examples are taken from our own work in the V erbmobil speech-to-speech translation system and in the EVAR train timetable dialogue system . In a ` prosodic orbit ' , we first present units , phenomena , annotations and statistical methods from the signal ( acoustics ) to the dialogue understanding phase . We show then , how prosody can be used together with other knowledge sources for the task of resegmentation if a first segmentation turns out to be wrong , and how an integrated approach leads to better results than a sequential use of the different knowledge sources ; then we present a hybrid approach which is used to perform a shallow parsing and which uses prosody to guide the parsing ; finally , we show how a critical system evaluation can help to improve the overall performance of automatic dialogue systems .
Spoken language understanding ( SLU ) is a key requirement of spoken dialogue systems ( SDS ) . The role of SLU parser is to robustly interpret the meanings of users ’ utterance using a hand-crafted grammar that is expensive to build .
Dialogue inferencing is the knowledge-intensive process of inferring aspects of a user 's problem from its partial description . Conversational case-based reasoning ( CCBR ) systems , which interactively and incrementally elicit a user 's problem description , suffer from poor retrieval efficiency ( i.e. , they prompt the user with questions that the user has already implicitly answered ) unless they perform dialogue inferencing . The standard method for dialogue inferencing in CCBR systems requires library designers to supply explicit inferencing rules .
Due to their simple implementation of CBR technology , these tools were almost ignored by the research community ( until recently ) , even though their use introduced many interesting applied research issues . We detail our progress on addressing three of these issues : simplifying case authoring , dialogue inferencing , and interactive planning . We describe evaluations of our approaches on these issues in the context of NaCoDAE and HICAP , our CCBR tools .
It presents a novel model for automatic generation of dialogue systems built from contents . This approach is similar to and relies on a search engine , but with augmented dialogue capabilities : at each dialogue turn , the system propose n keywords , in order to optimise the information gain expectation .
However , the performance is not satisfactory : the neural network tends to generate safe , universally relevant replies which carry little meaning . In this paper , we propose a content-introducing approach to neural network-based generative dialogue systems . We first use pointwise mutual information ( PMI ) to predict a noun as a keyword , reflecting the main gist of the reply .
By comparing the dialogues under two conditions , it seems that there are two types of head movements , one is intended to give a response to his partner and the other is to send some signal . We analyze how the visual information contributes in spoken dialogue perception , and the possibility of adopting it in a multi-modal human interface .
If argumentation-based dialogues are considered as communicative games between two or more agents , speech acts can be viewed as their building blocks . They are used to compose complex dialogues , such as persuasion , deliberation , information seeking , negotiation or inquiry and can be seen as the underlying reactive layer of communication ( see [ 35 ] for the definitions of various dialogue types , and [ 1,4,10,14,16,17,29,30,32 ] for investigations in multi-agent argumentation-based dialogue ) . In the process of exchanging messages , we naturally treat the sender and the receiver as two independent information sources , which try to expand , update , and revise their beliefs through communication .
We show how Bayesian networks and related probabilistic methods provide an efficient way of capturing the complex balancing of different factors that determine interpretation and generation in dialogue . As a case study , we show how a probabilistic approach can be used to model anaphora resolution in dialogue .
This research was Classroom Action Research . This research was aimed for ( 1 ) improving the quality of teaching in writing dialogue ability in unggah-ungguh basa and ( 2 ) improving the result quality of writing dialogue ability inunggah-ungguh basa . The Classroom Action Research was done in SMP Negeri 2 Kebakkramat .
This paper focuses on the design and evaluation of SARA , a conversational agent for the touristic domain featuring a high number of different , unique characteristics : spoken dialogue interaction , dialogue orchestration , context dependent information , an animated avatar and support for different kind of dialogue types , i.e. chat , specific and general question-answering , task oriented dialogues . The agent has currently two implementations : as web client and as mobile phone application .
Methods for domain independent dialogue systems have been proposed in previous research , however each of them has its own limitations . This paper presents a new approach , a task ontology model for domain independent dialogue management . An abstract task ontology is developed and based on that a generic dialogue manager is created .
Hierarchical Text Generation using an Outline Question Rewrite Based Dialogue Response Generation Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation Applying politeness maxims in social robotics polite dialogue Visual design consultant Evaluating Combinations of Dialogue Acts for Generation POLLy: A Conversational System that uses a Shared Representation to Generate Action and Social Language The Phonetic Representation of Spoken Language in Modern Hebrew Literature Book review: Explanation and Interaction: The Computer Generation of Explanatory Dialogues by Alison Cawsey (MIT Press. 1992) Generative Neural Machine for Tree Structures Concrete Assignments for Teaching NLP in an M.S. Program Augmenting Dialogue Response Generation With Unstructured Textual Knowledge Sequence-to-Sequence Learning as Beam-Search Optimization Method for predicting utterances using a layered plan recognition model - disambiguation of speech recognition candidates by focusing on speaker intentions Grounding in Electronic Discussions: Standard (Threaded) Versus Anchored Discussion Analysis and Synthesis of Tutorial Dialogues Using the conversation analytic role-play method in healthcare interpreter education A study on the Dialogue Interpretation Based on Triangular Model Dialogue Act Recognition via CRF-Attentive Structured Network Context-Based Incremental Generation for Dialogue Incrementality, Alignment and Shared Utterances Addressee Backchannels Can Bias Third-Party Memory and Judgment Turn Segmentation into Utterances for Arabic Spontaneous Dialogues and Instance Messages On the Importance of Word Order Information in Cross-lingual Sequence Labeling. On the use of prosody in automatic dialogue understanding SMART GRAMMAR : A DYNAMIC SPOKEN LANGUAGE UNDERSTANDING GRAMMAR FOR INFLECTIVE LANGUAGES Supporting Dialogue Inferencing in Conversational Case-Based Reasoning Conversational Case-Based Reasoning Content finder AssistanT Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation Difference in visual information between face to face and telephone dialogues A paraconsistent approach to speech acts Probabilistic Dialogue Modelling Peningkatan Kemampuan Menulis Dialog sesuai Unggah-Ungguh Basa melalui Penggunaan Media Kartu Tokoh pada Siswa Kelas VII C SMP Negeri 2 Kebakkramat Design and evaluation of a conversational agent for the touristic domain A task ontology model for domain independent dialogue management
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ICONIP (6) HRI SIGdial IJCNLP EMNLP CSCL INLG CogSci EWCBR ICIN ICASSP ArgMAS'2012: 9th International Workshop on Argumentation in Multi-Agent Systems SIGDIAL Workshop APSIPA VECIMS
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