The new added corpus is determined by verifying proper candidates using semi-automatic methods . The augmented corpus is used for building improved models and self-evolution of the dialog system is possible by replacing the old models . We implemented the D3 toolkit using web-based technologies to provide a familiar environment to non-expert end-users . This book will appeal to all trainee and newly qualified primary teachers needing to achieve both the demands of a subject knowledge for QTS and a firm understanding of the National Literacy Strategy and new primary curriculum . New to this edition New chapter exploring the characteristics of talk and dialogic learning Increased emphasis on ICT and extended coverage of multimedia Extended section on phonics Increased ‘ cross-curricular studies ’ emphasis This chapter comments on some of the main themes addressed by the authors of this volume . The notion of dialogic learning is discussed both as an analytical and theoretical approach to understanding the nature of learning in complex societies , and as a pedagogical and philosophical premise for developing classroom practices that promote student-centred and participatory learning activities . The invention relates to a method for conducting dialog between human and computer by means of a self-learning system , comprising : receiving user inputs in natural spoken text , associating synonyms and key words and making word associations , analyzing the user inputs with respect to sentence structure and sentence construction and recognizing same , associating key phrases , determining and classifying the user intention , checking if a confirmation is required , carrying out logical processing in a reasoning-and-answer preparation with formulation of a decision on the further dialog development , formulation or the dialog course with possible generation of an answer , wherein in order to recognize the user intention the statistical relevance of the association with a target intention is determined while at the same time all meta-information is retained , the classification occurs by means of a classification algorithm for intention clarification , logic and speech understanding are linked by means of meta-information in the decision tree , feedback to the self-learning system is generated by using earlier user questioning , a query to the user as to whether the recognized intention was correctly understood is generated if the statistical relevance is below a limit value , in this case an answer of the user is received and is then analyzed with respect to sentence structure and sentence construction and the user inputs are again divided into substantive content and into meta-information about the user and the user intention is again determined and classified , the recognition result is output to the self-learning system , the self-learning system develops automatic system recommendations and/or decisions for improving the intention recognition across a plurality of user interactions and keeps same at hand for later use , initiates the generation of the spoken-text answer to the user if the statistical relevance exceeds a limit value , and otherwise triggers a further inquiry . 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 built a videoconference system called the Virtual Auditorium to support dialog-based distance learning . The instructor can see dozens of students on a tiled wall-sized display and establish eye contact with any student . This kind of supervision is not realistic of how humans learn , where language is both learned by , and used for , communication . In this work , we study dialog-based language learning , where supervision is given naturally and implicitly in the response of the dialog partner during the conversation . We study this setup in two domains : the bAbI dataset of ( Weston et al. , 2015 ) and large-scale question answering from ( Dodge et al. , 2015 ) . Middle school teachers should develop their students ' inter-cultural awareness while conducting language teaching . This article intends to propose some measures and methods through vocabulary teaching , dialog teaching , text teaching and multi-media teaching in how to introduce culture in the English teaching in classrooms . A method for constructing a dialog example database for a dialog type foreign language learning , and a system and a method for a dialog type foreign language learning service using the same are provided to enhance the language learning capability of a learner such as the dialog capability of the foreign languages . A system for a dialog type foreign language learning service using a dialog example database comprises a dialog example storage unit(40 ) , an inputting processor(21 ) , a dialog management unit(22 ) and an outputting processor(23 ) . Semantic role labeling ( SRL ) extracts a high - level representation of meaning from a sentence , labeling e.g. who did what to whom . Explicit representations of such semantic information have been shown to improve results in challenging downstream tasks such as dialog systems tur2005semi , chen2013unsupervised , machine reading berant2014modeling , wang2015machine and translation liu2010semantic , bazrafshan2013semantic . Though syntax was long considered an obvious prerequisite for SRL systems levin1993english , punyakanok2008importance , recently deep neural network architectures have surpassed syntactically - informed models zhou2015end , marcheggiani2017simple , he2017deep , tan2018deep , he2018jointly , achieving state - of - the art SRL performance with no explicit modeling of syntax . Model selects the most probable response from 4 , 212 candidates which are ranked from a set of all bot utterances appearing in training , validation and test sets ( plain and OOV ) for all tasks combined . We also report results when we use match type features for dialog . Match type feature is an additional label on the candidates indicating if word is found on the dialog history . To handle questions with multiple - word answers , we consider each of them as a single word that contains punctuations such as space and comma , and put it in the vocabulary . Output Module for dialog . We use a fixed number single - layer softmax classifiers , each of which is similar to that of the sotry - based QA model , to sequentially output each word of the system 's response . Our fast architecture is interesting for character level encoders where the input is significantly longer than for words . Also , we plan to investigate the effectiveness of our architecture on other sequence - tosequence tasks , e.g. summarization , constituency parsing , dialog modeling . section : A Alignment Visualization In particular , the Google Search Engine is used to extract information from the Facebook social network . Keywords — Spoken Dialog System , Spoken Language Understanding , Web Semantic , Name Entity Recognition . We explore the use of lexicons in neural models for slot tagging in spoken language understanding . We develop models that encode lexicon information as features for use in a Longshort term memory neural network . It is thus taken for granted that speech recognition is the prerequisite to speech understanding . The human processes of spoken language comprehension , on the contrary , are rather holistic and do not necessarily require identification of every constituent unit . This is possible since the listener has the capability of constructing the expected message while listening , and of matching it against the input message on the basis of global features , without paying much attention to the details . Recent progress in the eld of spoken natural language understanding expanded the scope of spoken language systems to include mixed initiative dialogue . Currently there are no agreed upon theoretical foundations for the design of such systems . Developing a spontaneous speech corpus would be beneficial for spoken language processing and understanding . We present a speech corpus named the SMASH corpus , which includes spontaneous speech of two Japanese male commentators that made third-person audio commentaries during the gameplay of a fighting game . This paper presents a novel approach to spoken language understand- ing in dialogue systems . Unlike prevalent methods that use only the word lattices , the presented approach works with phoneme lattices generated by a phoneme recognizer . Despite its current incomplete implementation , we have observed that speech and pen have the potential to significantly improve user experience in our user study reported in this paper . We describe in this system-oriented paper the main components of MiPad , with a focus on the robust speech processing and spoken language understanding aspects . The detailed MiPad components discussed include : distributed speech recognition considerations for the speech processing algorithm design ; a stereo-based speech feature enhancement algorithm used for noise-robust front-end speech processing ; Aurora2 evaluation results for this front-end processing ; speech feature compression ( source coding ) and error protection ( channel coding ) for distributed speech recognition in MiPad ; HMM-based acoustic modeling for continuous speech recognition decoding ; a unified language model integrating context-free grammar and N-gram model for the speech decoding ; schema-based knowledge representation for the MiPad 's personal information management task ; a unified statistical framework that integrates speech recognition , spoken language understanding and dialogue management ; the robust natural language parser used in MiPad to process the speech recognizer 's output ; a machine-aided grammar learning and development used for spoken language understanding for the MiPad task ; Tap & Talk multimodal interaction and user interface design ; back channel communication and MiPad 's error repair strategy ; and finally , user study results that demonstrate the superior throughput achieved by the Tap & Talk multimodal interaction over the existing pen-only PDA interface . The results are promising since this end-to-end approach provides similar results ( F-measure= 0.66 on test data ) than a classical pipeline approach to detect named entity categories ( F-measure=0.64 ) . Last , we also explore this approach applied to semantic concept extraction , through a slot filling task known as a spoken language understanding problem , and also observe an improvement in comparison to a pipeline approach . This paper describes the first results achieved within the LUNA project in coupling the Spoken Language Understanding process with the Automatic Speech Recognition and Dialog Manager processes . This strategy is implemented and evaluated on a France Telecom telephone service application called FT3000 . It incorporates a built-in microphone that activates whenever a field is selected . As a user taps the screen or uses a built in roller to navigate , the tapping action narrows the number of possible instructions for spoken word understanding . MiPad currently runs on a Windows CE Pocket PC with a Windows 2000 machine where speech recognition is performed . While linking language with perception is currently a topical issue , there has been relatively little work on linking grounding directly with inference . By drawing closer to a joint solution to the grounding and inference tasks , models will also be better able to address language understanding in the real world . The present paper presented a first step in this direction using a version of an existing TE dataset which was augmented with images that could be paired directly with the premises , since these were originally captions for those images . In this work we explore recent advances in Recurrent Neural Networks for large scale Language Modeling , a task central to language understanding . We extend current models to deal with two key challenges present in this task : corpora and vocabulary sizes , and complex , long term structure of language . document : Snips Voice Platform : an embedded Spoken Language Understanding system for private - by - design voice interfaces This paper presents the machine learning architecture of the Snips Voice Platform , a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices . The embedded inference is fast and accurate while enforcing privacy by design , as no personal user data is ever collected . This paper concerns a design , application , and testing of a Rogerian psychologist strategy for information elicitation in dialogue management . Our results show that keeping the initiative in dialogue at users ’ side as long as they know what to say improves information exchange rate by 37.5 % . It uses Google server for recognition and Google text to speech for speech synthesis provided with Android system . Dialogue understanding , dialogue managing and text generating modules reside on a remote computer . Ten volunteers ' users have tested our system and a score of 3.6 on Likert scale was obtained . The semantic mapping function is determined by a visualisation system in a dialogue manager , and this leads to an idea of a very dynamic interaction which adapts naturally to the requirements of the individual user . Several correlative issues in dialogue managing are discussed and a prototype implementation is described . This work shows how a spoken dialogue system can be represented as a Partially Observable Markov Decision Process ( POMDP ) with composite observations consisting of discrete elements representing dialogue acts and continuous components representing confidence scores . Using a testbed simulated dialogue management problem and recently developed optimisation techniques , we demonstrate that this continuous POMDP can outperform traditional approaches in which confidence score is tracked discretely . Further , we present a method for automatically improving handcrafted dialogue managers by incorporating POMDP belief state monitoring , including confidence score information . With RadSpeech , we aim to build the next generation of intelligent , scalable , and user-friendly semantic search interfaces for the medical imaging domain , based on semantic technologies . Ontology-based knowledge representation is used not only for the image contents , but also for the complex natural language understanding and dialogue management process . This demo shows a speech-based annotation system for radiology images and focuses on a new and effective way to annotate medical image regions with a specific medical , structured , diagnosis while using speech and pointing gestures on the go . This article describes a method for classifying dialogue ut terances and detecting the interlocutor 's agreement or disagreement . This labellingcan help improve dialogue manage- ment by providing additional information on the utterance ' s content without deep parsing . The proposed technique improves upon state of the art approache s by using a Support Vector Ma- chine cascade . We present VOnDA , a framework to implement the dialogue management functionality in dialogue systems . Although domain-independent , VOnDA is tailored towards dialogue systems with a focus on social communication , which implies the need of long-term memory and high user adaptivity . There are several challenges in applying conversational social robots to Technology Enhanced Learning and Serious Gaming . In this paper , we focus in particular on the dialogue management issues in building an empathic robotic tutor that plays a multi-person serious game with students to help them learn and understand the underlying educational concepts . Categories and Subject Descriptors I.2.11 [ Intelligent Agents ] : [ Dialogue management ] While the WOz method can help design robot behaviors , traditional approaches place the burden of decisions on a single wizard . In this work , we consider two wizards to stand in for robot navigation and dialogue management software components . The scenario used to elicit data is one in which a human-robot team is tasked with exploring an unknown environment : a human gives verbal instructions from a remote location and the robot follows them , clarifying possible misunderstandings as needed via dialogue . When a real-time information update is requested from the dialogue management unit , a real-time automatic knowledge constructing unit(70 ) extracts daily life information updated on a web in real time and stores the extracted information in a daily information database . A response generating unit(40 ) generates a response for the request of the user analyzed in the dialogue management unit and provides the response to the user . When it is impossible to respond to the request of the user or an error is generated in the response for the request of the user , an exception processing unit(80 ) recovers a system to allow the user to cancel the request of the user . section : Abstract Automatically generating coherent and semantically meaningful text has many applications in machine translation , dialogue systems , image captioning , etc . Recently , by combining with policy gradient , Generative Adversarial Nets ( GAN ) that use a discriminative model to guide the training of the generative model as a reinforcement learning policy has shown promising results in text generation . section : Related Work Generating text that mimics human 's expression has been studied for poem generation [ reference ] , image captioning , dialogue system ) machine translation . [ reference ] proposed a recurent neural network ( RNN ) based generative model to use the human - generated text where at each step the model tries to predict the next word given previous real word sequence and is trained in a supervised fashion . bAbI dialog dataset bAbI dialog dataset [ reference ] ) is a set of 5 tasks within the goal - oriented context of restaurant reservation . It is designed to test if model can learn various abilities such as performing dialog management , querying knowledge bases ( KBs ) , and interpreting the output of such queries . The KB can be queried using API calls and 4 fields ( a type of cuisine , a location , a price range , and a party size ) . Reinforcement learning is becoming a popular tool for building dialogue managers . This paper addresses two issues in using RL . In this paper , we describe the steps carried out for developing a Railway Information Service for Spanish . This work introduces a methodology for designing dialogue managers in spoken dialogue systems for restricted domains . In this methodology several sources of information are combined : intuition , observation and simulation for defining several dialogue strategies , evaluating them and choosing the best . Argumentation has proved to be a very influential reasoning mechanism particularly in the context of multi agent systems . In this paper we introduce PADUA ( Protocol for Argumentation Dialogue Using Association Rules ) , a novel argumentation formalism that dynamically mines Association Rules ( ARs ) from the case background as a means to : ( i ) generate the arguments exchanged among dialogue participants , and ( ii ) represent each participant ’s background domain knowledge , thus avoiding the traditional knowledge base representations . Dialogue participants mine ARs from their own case data and then use these rules as arguments and counter arguments . However , modeling realistic action and turn-taking in more general scenarios remains a challenge . In this paper we propose a generic framework to aid in development of multi-modal , multi-party dialogue . It contains mechanisms inspired by social practice theory for both action selection and timing --- including handling of interruption . This paper describes an approach to contextual reasoning for interpretation of spoken multimodal dialogue . The approach is based on combining recencybased search for antecedents with an object-oriented domain representation in such a way that the search is highly constrained by the type information of the antecedents . In multiparty multimodal dialogue setup , where the robot is set tointeract with multiple people , a main requirement for the robot is to recognizethe user speaking to it . This would allow the robot to pay attention(visually ) to the person the robot is listening to ( for example looking by thegaze and head pose to the speaker ) , and to organize the dialogue structurewith multiple people . In the biomedical domain , the lack of sharable datasets often limit the possibility of developing natural language processing systems , especially dialogue applica- tions and natural language understanding models . To overcome this issue , we explore data generation using templates and terminologies and data augmentation approaches . Hypothesis ranking ( HR ) is an approach for improving the accuracy of both domain detection and tracking in multi-domain , multi-turn dialogue systems . This paper presents the results of applying a universal HR model to multiple dialogue systems , each of which are using a different language . The solution should be preferably implemented using existing voice browser . To achieve the object , at least one held on the application server , usable in the speech dialogue application is associated with a Emotionserkenner module for a prosodic classification of the voice inputs of the user . The application in question comprises a control logic , by which its response is controlled to a voice input in response to both of the control commands obtained by means of the semantic analysis of language and a signal obtained from the Emotionserkenner module , representing the emotional state of the user value . A New Architecture for Health-Care Dialogue Systems . Spoken dialogue systems have been increasingly employed to provide ubiquitous automated access via telephone to information and services for the non-Internet-connected public . We describe a dialogue evaluation plan for a multi-character virtual reality training simulation . A multi-component evaluation plan is presented , including user satisfaction , intended task completion , recognition rate , and a new annotation scheme for appropriateness . section : Introduction The ability to generate coherent and semantically meaningful text plays a key role in many natural language processing applications such as machine translation , dialogue generation , and image captioning [ reference ] . While most previous work focuses on task - specific applications in supervised settings [ reference ][ reference ] , the generic unsupervised text generation , which aims to mimic the distribution over real text from a corpus , has recently drawn much attention [ reference ][ reference ][ reference ] ; It should be noted that while related in name , adversarial training ( AT ) differs from Generative Adversarial Networks ( GANs ) . GANs have already been applied to NLP tasks such as dialogue generation and transfer learning . Adversarial training also differs from adversarial evaluation , recently proposed for reading comprehension tasks . document : Global - Locally Self - Attentive Dialogue State Tracker Dialogue state tracking , which estimates user goals and requests given the dialogue context , is an essential part of task - oriented dialogue systems . In this paper , we propose the Global - Locally Self - Attentive Dialogue State Tracker ( GLAD ) , which learns representations of the user utterance and previous system actions with global - local modules . In this work , we propose the G lobal - L ocally Self - A ttentive D ialogue State Tracker ( GLAD ) , a new state - of - the - art model for dialogue state tracking . In contrast to previous work that estimate each slot - value pair independently , GLAD uses global modules to share parameters between estimators for each slot and local modules to learn slot - specific feature representations . 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 . 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 .
The new added corpus is determined by verifying proper candidates using semi-automatic methods . The augmented corpus is used for building improved models and self-evolution of the dialog system is possible by replacing the old models . We implemented the D3 toolkit using web-based technologies to provide a familiar environment to non-expert end-users .
This book will appeal to all trainee and newly qualified primary teachers needing to achieve both the demands of a subject knowledge for QTS and a firm understanding of the National Literacy Strategy and new primary curriculum . New to this edition New chapter exploring the characteristics of talk and dialogic learning Increased emphasis on ICT and extended coverage of multimedia Extended section on phonics Increased ‘ cross-curricular studies ’ emphasis
This chapter comments on some of the main themes addressed by the authors of this volume . The notion of dialogic learning is discussed both as an analytical and theoretical approach to understanding the nature of learning in complex societies , and as a pedagogical and philosophical premise for developing classroom practices that promote student-centred and participatory learning activities .
The invention relates to a method for conducting dialog between human and computer by means of a self-learning system , comprising : receiving user inputs in natural spoken text , associating synonyms and key words and making word associations , analyzing the user inputs with respect to sentence structure and sentence construction and recognizing same , associating key phrases , determining and classifying the user intention , checking if a confirmation is required , carrying out logical processing in a reasoning-and-answer preparation with formulation of a decision on the further dialog development , formulation or the dialog course with possible generation of an answer , wherein in order to recognize the user intention the statistical relevance of the association with a target intention is determined while at the same time all meta-information is retained , the classification occurs by means of a classification algorithm for intention clarification , logic and speech understanding are linked by means of meta-information in the decision tree , feedback to the self-learning system is generated by using earlier user questioning , a query to the user as to whether the recognized intention was correctly understood is generated if the statistical relevance is below a limit value , in this case an answer of the user is received and is then analyzed with respect to sentence structure and sentence construction and the user inputs are again divided into substantive content and into meta-information about the user and the user intention is again determined and classified , the recognition result is output to the self-learning system , the self-learning system develops automatic system recommendations and/or decisions for improving the intention recognition across a plurality of user interactions and keeps same at hand for later use , initiates the generation of the spoken-text answer to the user if the statistical relevance exceeds a limit value , and otherwise triggers a further inquiry .
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 built a videoconference system called the Virtual Auditorium to support dialog-based distance learning . The instructor can see dozens of students on a tiled wall-sized display and establish eye contact with any student .
This kind of supervision is not realistic of how humans learn , where language is both learned by , and used for , communication . In this work , we study dialog-based language learning , where supervision is given naturally and implicitly in the response of the dialog partner during the conversation . We study this setup in two domains : the bAbI dataset of ( Weston et al. , 2015 ) and large-scale question answering from ( Dodge et al. , 2015 ) .
Middle school teachers should develop their students ' inter-cultural awareness while conducting language teaching . This article intends to propose some measures and methods through vocabulary teaching , dialog teaching , text teaching and multi-media teaching in how to introduce culture in the English teaching in classrooms .
A method for constructing a dialog example database for a dialog type foreign language learning , and a system and a method for a dialog type foreign language learning service using the same are provided to enhance the language learning capability of a learner such as the dialog capability of the foreign languages . A system for a dialog type foreign language learning service using a dialog example database comprises a dialog example storage unit(40 ) , an inputting processor(21 ) , a dialog management unit(22 ) and an outputting processor(23 ) .
Semantic role labeling ( SRL ) extracts a high - level representation of meaning from a sentence , labeling e.g. who did what to whom . Explicit representations of such semantic information have been shown to improve results in challenging downstream tasks such as dialog systems tur2005semi , chen2013unsupervised , machine reading berant2014modeling , wang2015machine and translation liu2010semantic , bazrafshan2013semantic . Though syntax was long considered an obvious prerequisite for SRL systems levin1993english , punyakanok2008importance , recently deep neural network architectures have surpassed syntactically - informed models zhou2015end , marcheggiani2017simple , he2017deep , tan2018deep , he2018jointly , achieving state - of - the art SRL performance with no explicit modeling of syntax .
Model selects the most probable response from 4 , 212 candidates which are ranked from a set of all bot utterances appearing in training , validation and test sets ( plain and OOV ) for all tasks combined . We also report results when we use match type features for dialog . Match type feature is an additional label on the candidates indicating if word is found on the dialog history .
To handle questions with multiple - word answers , we consider each of them as a single word that contains punctuations such as space and comma , and put it in the vocabulary . Output Module for dialog . We use a fixed number single - layer softmax classifiers , each of which is similar to that of the sotry - based QA model , to sequentially output each word of the system 's response .
Our fast architecture is interesting for character level encoders where the input is significantly longer than for words . Also , we plan to investigate the effectiveness of our architecture on other sequence - tosequence tasks , e.g. summarization , constituency parsing , dialog modeling . section : A Alignment Visualization
In particular , the Google Search Engine is used to extract information from the Facebook social network . Keywords — Spoken Dialog System , Spoken Language Understanding , Web Semantic , Name Entity Recognition .
We explore the use of lexicons in neural models for slot tagging in spoken language understanding . We develop models that encode lexicon information as features for use in a Longshort term memory neural network .
It is thus taken for granted that speech recognition is the prerequisite to speech understanding . The human processes of spoken language comprehension , on the contrary , are rather holistic and do not necessarily require identification of every constituent unit . This is possible since the listener has the capability of constructing the expected message while listening , and of matching it against the input message on the basis of global features , without paying much attention to the details .
Recent progress in the eld of spoken natural language understanding expanded the scope of spoken language systems to include mixed initiative dialogue . Currently there are no agreed upon theoretical foundations for the design of such systems .
Developing a spontaneous speech corpus would be beneficial for spoken language processing and understanding . We present a speech corpus named the SMASH corpus , which includes spontaneous speech of two Japanese male commentators that made third-person audio commentaries during the gameplay of a fighting game .
This paper presents a novel approach to spoken language understand- ing in dialogue systems . Unlike prevalent methods that use only the word lattices , the presented approach works with phoneme lattices generated by a phoneme recognizer .
Despite its current incomplete implementation , we have observed that speech and pen have the potential to significantly improve user experience in our user study reported in this paper . We describe in this system-oriented paper the main components of MiPad , with a focus on the robust speech processing and spoken language understanding aspects . The detailed MiPad components discussed include : distributed speech recognition considerations for the speech processing algorithm design ; a stereo-based speech feature enhancement algorithm used for noise-robust front-end speech processing ; Aurora2 evaluation results for this front-end processing ; speech feature compression ( source coding ) and error protection ( channel coding ) for distributed speech recognition in MiPad ; HMM-based acoustic modeling for continuous speech recognition decoding ; a unified language model integrating context-free grammar and N-gram model for the speech decoding ; schema-based knowledge representation for the MiPad 's personal information management task ; a unified statistical framework that integrates speech recognition , spoken language understanding and dialogue management ; the robust natural language parser used in MiPad to process the speech recognizer 's output ; a machine-aided grammar learning and development used for spoken language understanding for the MiPad task ; Tap & Talk multimodal interaction and user interface design ; back channel communication and MiPad 's error repair strategy ; and finally , user study results that demonstrate the superior throughput achieved by the Tap & Talk multimodal interaction over the existing pen-only PDA interface .
The results are promising since this end-to-end approach provides similar results ( F-measure= 0.66 on test data ) than a classical pipeline approach to detect named entity categories ( F-measure=0.64 ) . Last , we also explore this approach applied to semantic concept extraction , through a slot filling task known as a spoken language understanding problem , and also observe an improvement in comparison to a pipeline approach .
This paper describes the first results achieved within the LUNA project in coupling the Spoken Language Understanding process with the Automatic Speech Recognition and Dialog Manager processes . This strategy is implemented and evaluated on a France Telecom telephone service application called FT3000 .
It incorporates a built-in microphone that activates whenever a field is selected . As a user taps the screen or uses a built in roller to navigate , the tapping action narrows the number of possible instructions for spoken word understanding . MiPad currently runs on a Windows CE Pocket PC with a Windows 2000 machine where speech recognition is performed .
While linking language with perception is currently a topical issue , there has been relatively little work on linking grounding directly with inference . By drawing closer to a joint solution to the grounding and inference tasks , models will also be better able to address language understanding in the real world . The present paper presented a first step in this direction using a version of an existing TE dataset which was augmented with images that could be paired directly with the premises , since these were originally captions for those images .
In this work we explore recent advances in Recurrent Neural Networks for large scale Language Modeling , a task central to language understanding . We extend current models to deal with two key challenges present in this task : corpora and vocabulary sizes , and complex , long term structure of language .
document : Snips Voice Platform : an embedded Spoken Language Understanding system for private - by - design voice interfaces This paper presents the machine learning architecture of the Snips Voice Platform , a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices . The embedded inference is fast and accurate while enforcing privacy by design , as no personal user data is ever collected .
This paper concerns a design , application , and testing of a Rogerian psychologist strategy for information elicitation in dialogue management . Our results show that keeping the initiative in dialogue at users ’ side as long as they know what to say improves information exchange rate by 37.5 % .
It uses Google server for recognition and Google text to speech for speech synthesis provided with Android system . Dialogue understanding , dialogue managing and text generating modules reside on a remote computer . Ten volunteers ' users have tested our system and a score of 3.6 on Likert scale was obtained .
The semantic mapping function is determined by a visualisation system in a dialogue manager , and this leads to an idea of a very dynamic interaction which adapts naturally to the requirements of the individual user . Several correlative issues in dialogue managing are discussed and a prototype implementation is described .
This work shows how a spoken dialogue system can be represented as a Partially Observable Markov Decision Process ( POMDP ) with composite observations consisting of discrete elements representing dialogue acts and continuous components representing confidence scores . Using a testbed simulated dialogue management problem and recently developed optimisation techniques , we demonstrate that this continuous POMDP can outperform traditional approaches in which confidence score is tracked discretely . Further , we present a method for automatically improving handcrafted dialogue managers by incorporating POMDP belief state monitoring , including confidence score information .
With RadSpeech , we aim to build the next generation of intelligent , scalable , and user-friendly semantic search interfaces for the medical imaging domain , based on semantic technologies . Ontology-based knowledge representation is used not only for the image contents , but also for the complex natural language understanding and dialogue management process . This demo shows a speech-based annotation system for radiology images and focuses on a new and effective way to annotate medical image regions with a specific medical , structured , diagnosis while using speech and pointing gestures on the go .
This article describes a method for classifying dialogue ut terances and detecting the interlocutor 's agreement or disagreement . This labellingcan help improve dialogue manage- ment by providing additional information on the utterance ' s content without deep parsing . The proposed technique improves upon state of the art approache s by using a Support Vector Ma- chine cascade .
We present VOnDA , a framework to implement the dialogue management functionality in dialogue systems . Although domain-independent , VOnDA is tailored towards dialogue systems with a focus on social communication , which implies the need of long-term memory and high user adaptivity .
There are several challenges in applying conversational social robots to Technology Enhanced Learning and Serious Gaming . In this paper , we focus in particular on the dialogue management issues in building an empathic robotic tutor that plays a multi-person serious game with students to help them learn and understand the underlying educational concepts . Categories and Subject Descriptors I.2.11 [ Intelligent Agents ] : [ Dialogue management ]
While the WOz method can help design robot behaviors , traditional approaches place the burden of decisions on a single wizard . In this work , we consider two wizards to stand in for robot navigation and dialogue management software components . The scenario used to elicit data is one in which a human-robot team is tasked with exploring an unknown environment : a human gives verbal instructions from a remote location and the robot follows them , clarifying possible misunderstandings as needed via dialogue .
When a real-time information update is requested from the dialogue management unit , a real-time automatic knowledge constructing unit(70 ) extracts daily life information updated on a web in real time and stores the extracted information in a daily information database . A response generating unit(40 ) generates a response for the request of the user analyzed in the dialogue management unit and provides the response to the user . When it is impossible to respond to the request of the user or an error is generated in the response for the request of the user , an exception processing unit(80 ) recovers a system to allow the user to cancel the request of the user .
section : Abstract Automatically generating coherent and semantically meaningful text has many applications in machine translation , dialogue systems , image captioning , etc . Recently , by combining with policy gradient , Generative Adversarial Nets ( GAN ) that use a discriminative model to guide the training of the generative model as a reinforcement learning policy has shown promising results in text generation .
section : Related Work Generating text that mimics human 's expression has been studied for poem generation [ reference ] , image captioning , dialogue system ) machine translation . [ reference ] proposed a recurent neural network ( RNN ) based generative model to use the human - generated text where at each step the model tries to predict the next word given previous real word sequence and is trained in a supervised fashion .
bAbI dialog dataset bAbI dialog dataset [ reference ] ) is a set of 5 tasks within the goal - oriented context of restaurant reservation . It is designed to test if model can learn various abilities such as performing dialog management , querying knowledge bases ( KBs ) , and interpreting the output of such queries . The KB can be queried using API calls and 4 fields ( a type of cuisine , a location , a price range , and a party size ) .
Reinforcement learning is becoming a popular tool for building dialogue managers . This paper addresses two issues in using RL .
In this paper , we describe the steps carried out for developing a Railway Information Service for Spanish . This work introduces a methodology for designing dialogue managers in spoken dialogue systems for restricted domains . In this methodology several sources of information are combined : intuition , observation and simulation for defining several dialogue strategies , evaluating them and choosing the best .
Argumentation has proved to be a very influential reasoning mechanism particularly in the context of multi agent systems . In this paper we introduce PADUA ( Protocol for Argumentation Dialogue Using Association Rules ) , a novel argumentation formalism that dynamically mines Association Rules ( ARs ) from the case background as a means to : ( i ) generate the arguments exchanged among dialogue participants , and ( ii ) represent each participant ’s background domain knowledge , thus avoiding the traditional knowledge base representations . Dialogue participants mine ARs from their own case data and then use these rules as arguments and counter arguments .
However , modeling realistic action and turn-taking in more general scenarios remains a challenge . In this paper we propose a generic framework to aid in development of multi-modal , multi-party dialogue . It contains mechanisms inspired by social practice theory for both action selection and timing --- including handling of interruption .
This paper describes an approach to contextual reasoning for interpretation of spoken multimodal dialogue . The approach is based on combining recencybased search for antecedents with an object-oriented domain representation in such a way that the search is highly constrained by the type information of the antecedents .
In multiparty multimodal dialogue setup , where the robot is set tointeract with multiple people , a main requirement for the robot is to recognizethe user speaking to it . This would allow the robot to pay attention(visually ) to the person the robot is listening to ( for example looking by thegaze and head pose to the speaker ) , and to organize the dialogue structurewith multiple people .
In the biomedical domain , the lack of sharable datasets often limit the possibility of developing natural language processing systems , especially dialogue applica- tions and natural language understanding models . To overcome this issue , we explore data generation using templates and terminologies and data augmentation approaches .
Hypothesis ranking ( HR ) is an approach for improving the accuracy of both domain detection and tracking in multi-domain , multi-turn dialogue systems . This paper presents the results of applying a universal HR model to multiple dialogue systems , each of which are using a different language .
The solution should be preferably implemented using existing voice browser . To achieve the object , at least one held on the application server , usable in the speech dialogue application is associated with a Emotionserkenner module for a prosodic classification of the voice inputs of the user . The application in question comprises a control logic , by which its response is controlled to a voice input in response to both of the control commands obtained by means of the semantic analysis of language and a signal obtained from the Emotionserkenner module , representing the emotional state of the user value .
A New Architecture for Health-Care Dialogue Systems . Spoken dialogue systems have been increasingly employed to provide ubiquitous automated access via telephone to information and services for the non-Internet-connected public .
We describe a dialogue evaluation plan for a multi-character virtual reality training simulation . A multi-component evaluation plan is presented , including user satisfaction , intended task completion , recognition rate , and a new annotation scheme for appropriateness .
section : Introduction The ability to generate coherent and semantically meaningful text plays a key role in many natural language processing applications such as machine translation , dialogue generation , and image captioning [ reference ] . While most previous work focuses on task - specific applications in supervised settings [ reference ][ reference ] , the generic unsupervised text generation , which aims to mimic the distribution over real text from a corpus , has recently drawn much attention [ reference ][ reference ][ reference ] ;
It should be noted that while related in name , adversarial training ( AT ) differs from Generative Adversarial Networks ( GANs ) . GANs have already been applied to NLP tasks such as dialogue generation and transfer learning . Adversarial training also differs from adversarial evaluation , recently proposed for reading comprehension tasks .
document : Global - Locally Self - Attentive Dialogue State Tracker Dialogue state tracking , which estimates user goals and requests given the dialogue context , is an essential part of task - oriented dialogue systems . In this paper , we propose the Global - Locally Self - Attentive Dialogue State Tracker ( GLAD ) , which learns representations of the user utterance and previous system actions with global - local modules .
In this work , we propose the G lobal - L ocally Self - A ttentive D ialogue State Tracker ( GLAD ) , a new state - of - the - art model for dialogue state tracking . In contrast to previous work that estimate each slot - value pair independently , GLAD uses global modules to share parameters between estimators for each slot and local modules to learn slot - specific feature representations .
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 .
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 .
D3 Toolkit: A Development Toolkit for Daydreaming Spoken Dialog Systems Language knowledge for primary teachers. 4th edition Notes on Classroom Practices, Dialogicality, and the Transformation of Learning Method for conducting dialog between human and computer End-to-End Question Answering Models for Goal-Oriented Dialog Learning Design of a virtual auditorium Dialog-based Language Learning Introducing Culture in the English Teaching in Middle Schools Interactive dialog database construction method for foreign language learning, system and method of interactive service for foreign language learning using its Linguistically-Informed Self-Attention for Semantic Role Labeling Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning Query-Reduction Networks for Question Answering A Convolutional Encoder Model for Neural Machine Translation A Web-Based Self-Learning Grammar for Spoken Language Understanding Neural Lexicons for Slot Tagging in Spoken Language Understanding What can we learn from human processes of spoken language comprehension A stochastic model of computer-human interaction for learning dialogue strategies SMASH Corpus: A Spontaneous Speech Corpus Recording Third-person Audio Commentaries on Gameplay On the Use of Phoneme Lattices in Spoken Language Understanding Speech and Language Processing for Multimodal Human-Computer Interaction End-To-End Named Entity And Semantic Concept Extraction From Speech First implementation of the LUNA Spoken Language Understanding strategy on a telephone service application MiPad: a multimodal interaction prototype Grounded Textual Entailment Exploring the Limits of Language Modeling Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces Applying Rogerian Psychologist in Human-Computer Interaction: A Case Study Ontology and parser dependencies approach for spoken dialogue system Knowledge-Based Graphical Dialogue: A Strategy and Architecture PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES WITH CONTINUOUS OBSERVATIONS FOR DIALOGUE MANAGEMENT RadSpeech's mobile dialogue system for radiologists A SVM Cascade for Agreement/Disagreement Classification VOnDA: A Framework for Ontology-Based Dialogue Management Towards a Serious Game Playing Empathic Robotic Tutorial Dialogue System Applying the Wizard-of-Oz Technique to Multimodal Human-Robot Dialogue Apparatus and method of dialogue tv agent service for providing daily information Long Text Generation via Adversarial Training with Leaked Information Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning Using Reinforcement Learning for Dialogue Management Policies: Towards Understanding MDP Violations and Convergence A Telephone-Based Railway Information System for Spanish: Development of a Methodology for Spoken Dialogue Design Dynamic Rule Mining for Argumentation Based Systems Towards a generic framework for multi-party dialogue with virtual humans Proceedings of the 8th Workshop on the Semantics and Pragmatics of Dialogue (Catalog) Towards Speaker Detectionusing FaceAPI Facial Movementsin Human-Machine Multiparty Dialogue Natural language understanding for task oriented dialog in the biomedical domain in a low ressources context, NIPS Workshop Multi-language hypotheses ranking and domain tracking for open domain dialogue systems The speech dialogue system and method for operating AdaRTE: Adaptable Dialogue Architecture and Runtime Engine. A New Architecture for Health-Care Dialogue Systems Evaluation of Multi-party Virtual Reality Dialogue Interaction Long Text Generation via Adversarial Training with Leaked Information Robust Multilingual Part-of-Speech Tagging via Adversarial Training Global-Locally Self-Attentive Dialogue State Tracker
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IWSDS ACM Multimedia EMNLP ArXiv ICLR ACL NAACL-HLT (2) EUROSPEECH LREC SLT ICASSP COLING ArXiv ArXiv MICAI (1) ISSPIT IUI HRI AAAI ArXiv INTERSPEECH SIGDIAL Workshop SGAI Conf. CASA INTERSPEECH MedInfo LREC AAAI NAACL-HLT ACL
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