In the paper we describe an approach to dialogue management in the agreement negotiation where one of the central roles is attributed to the model of natural human reasoning. The reasoning model consists of the model of human motivational sphere, and of reasoning algorithms. The reasoning model is interacting with the model of communication process. The latter is considered as rational activity where central role play the concepts of communicative strategies and tactics.
{"title":"Dialogue Management in the Agreement Negotiation Process: A Model that Involves Natural Reasoning","authors":"M. Koit, H. Õim","doi":"10.3115/1117736.1117748","DOIUrl":"https://doi.org/10.3115/1117736.1117748","url":null,"abstract":"In the paper we describe an approach to dialogue management in the agreement negotiation where one of the central roles is attributed to the model of natural human reasoning. The reasoning model consists of the model of human motivational sphere, and of reasoning algorithms. The reasoning model is interacting with the model of communication process. The latter is considered as rational activity where central role play the concepts of communicative strategies and tactics.","PeriodicalId":426429,"journal":{"name":"SIGDIAL Workshop","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123681132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since early 1998, the European Telematics project MATE has worked towards facilitating re-use of annotated spoken language data, addressing theoretical issues and implementing practical solutions which could serve as standards in the field. The resulting MATE Workbench for corpus annotation is now available as licensed open source software.This paper describes the MATE markup framework which bridges between the theoretical and the practical activities of MATE and is proposed as a standard for the definition and representation of markup for spoken dialogue corpora. We also present early experience from use of the framework.
{"title":"The MATE Markup Framework","authors":"L. Dybkjær, N. Bernsen","doi":"10.3115/1117736.1117739","DOIUrl":"https://doi.org/10.3115/1117736.1117739","url":null,"abstract":"Since early 1998, the European Telematics project MATE has worked towards facilitating re-use of annotated spoken language data, addressing theoretical issues and implementing practical solutions which could serve as standards in the field. The resulting MATE Workbench for corpus annotation is now available as licensed open source software.This paper describes the MATE markup framework which bridges between the theoretical and the practical activities of MATE and is proposed as a standard for the definition and representation of markup for spoken dialogue corpora. We also present early experience from use of the framework.","PeriodicalId":426429,"journal":{"name":"SIGDIAL Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122072273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes a Japanese dialogue corpus annotated with multi-level information built by the Japanese Discourse Research Initiative, Japanese Society for Artificial Intelligence. The annotation information consists of speech, transcription delimited by slash units, prosodic, part of speech, dialogue acts and dialogue segmentation. In the project, we used the corpus for obtaining new findings by examining the relationship between linguistic information and dialogue acts, that between prosodic information and dialogue segment, and the characteristics of agreement/disagreement expressions and non-sentence elements.
{"title":"Japanese Dialogue Corpus of Multi-Level Annotation","authors":"Shu Nakazato","doi":"10.3115/1117736.1117737","DOIUrl":"https://doi.org/10.3115/1117736.1117737","url":null,"abstract":"This paper describes a Japanese dialogue corpus annotated with multi-level information built by the Japanese Discourse Research Initiative, Japanese Society for Artificial Intelligence. The annotation information consists of speech, transcription delimited by slash units, prosodic, part of speech, dialogue acts and dialogue segmentation. In the project, we used the corpus for obtaining new findings by examining the relationship between linguistic information and dialogue acts, that between prosodic information and dialogue segment, and the characteristics of agreement/disagreement expressions and non-sentence elements.","PeriodicalId":426429,"journal":{"name":"SIGDIAL Workshop","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133660238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The learning and self-adaptive capability in dialog systems has become increasingly important with the advances in a wide range of applications. For any application, particularly the one dealing with a technical domain, the system should pay attention to not only the user experience level and dialog goals, but more importantly, the mechanism to adapt the system behavior to the evolving state of the user. This paper describes a methodology that first identifies the user experience level and utility metrics of the goal and sub-goals, then automatically adjusts those parameters based on discourse history and thus directs adaptive dialog management.
{"title":"Dynamic User Level and Utility Measurement for Adaptive Dialog in a Help-Desk System","authors":"Preetam Maloor, J. Chai","doi":"10.3115/1117736.1117747","DOIUrl":"https://doi.org/10.3115/1117736.1117747","url":null,"abstract":"The learning and self-adaptive capability in dialog systems has become increasingly important with the advances in a wide range of applications. For any application, particularly the one dealing with a technical domain, the system should pay attention to not only the user experience level and dialog goals, but more importantly, the mechanism to adapt the system behavior to the evolving state of the user. This paper describes a methodology that first identifies the user experience level and utility metrics of the goal and sub-goals, then automatically adjusts those parameters based on discourse history and thus directs adaptive dialog management.","PeriodicalId":426429,"journal":{"name":"SIGDIAL Workshop","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132743327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a model where dialog obligations arise from the interplay of social goals and intentions of the participants: when an agent is addressed with a request, the agent's decision to commit to the requester's linguistic and domain goals is motivated by a trade-off between the preference for preventing a negative reaction of the requester and the cost of the actions needed to satisfy the goals.
{"title":"Social Goals in Conversational Cooperation","authors":"G. Boella, R. Damiano, L. Lesmo","doi":"10.3115/1117736.1117746","DOIUrl":"https://doi.org/10.3115/1117736.1117746","url":null,"abstract":"We propose a model where dialog obligations arise from the interplay of social goals and intentions of the participants: when an agent is addressed with a request, the agent's decision to commit to the requester's linguistic and domain goals is motivated by a trade-off between the preference for preventing a negative reaction of the requester and the cost of the actions needed to satisfy the goals.","PeriodicalId":426429,"journal":{"name":"SIGDIAL Workshop","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114182798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article discusses the detection of discourse markers (DM) in dialog transcriptions, by human annotators and by automated means. After a theoretical discussion of the definition of DMs and their relevance to natural language processing, we focus on the role of like as a DM. Results from experiments with human annotators show that detection of DMs is a difficult but reliable task, which requires prosodic information from soundtracks. Then, several types of features are defined for automatic disambiguation of like: collocations, part-of-speech tags and duration-based features. Decision-tree learning shows that for like, nearly 70% precision can be reached, with near 100% recall, mainly using collocation filters. Similar results hold for well, with about 91% precision at 100% recall.
{"title":"Towards Automatic Identification of Discourse Markers in Dialogs: The Case of Like","authors":"S. Zufferey, Andrei Popescu-Belis","doi":"10.7892/BORIS.78686","DOIUrl":"https://doi.org/10.7892/BORIS.78686","url":null,"abstract":"This article discusses the detection of discourse \u0000 markers (DM) in dialog transcriptions, \u0000by human annotators and by automated \u0000means. After a theoretical discussion of the \u0000definition of DMs and their relevance to natural \u0000 language processing, we focus on the role \u0000of like as a DM. Results from experiments \u0000with human annotators show that detection of \u0000DMs is a difficult but reliable task, which requires \u0000 prosodic information from soundtracks. \u0000Then, several types of features are defined for \u0000automatic disambiguation of like: collocations, \u0000 part-of-speech tags and duration-based \u0000features. Decision-tree learning shows that for \u0000like, nearly 70% precision can be reached, \u0000with near 100% recall, mainly using collocation \u0000 filters. Similar results hold for well, with \u0000about 91% precision at 100% recall.","PeriodicalId":426429,"journal":{"name":"SIGDIAL Workshop","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122030601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kazunori Komatani, Naoyuki Kanda, Mikio Nakano, K. Nakadai, H. Tsujino, T. Ogata, HIroshi G. Okuno
We developed a multi-domain spoken dialogue system that can handle user requests across multiple domains. Such systems need to satisfy two requirements: extensibility and robustness against speech recognition errors. Extensibility is required to allow for the modification and addition of domains independent of other domains. Robustness against speech recognition errors is required because such errors are inevitable in speech recognition. However, the systems should still behave appropriately, even when their inputs are erroneous. Our system was constructed on an extensible architecture and is equipped with a robust and extensible domain selection method. Domain selection was based on three choices: (I) the previous domain, (II) the domain in which the speech recognition result can be accepted with the highest recognition score, and (III) other domains. With the third choice we newly introduced, our system can prevent dialogues from continuously being stuck in an erroneous domain. Our experimental results, obtained with 10 subjects, showed that our method reduced the domain selection errors by 18.3%, compared to a conventional method.
{"title":"Multi-Domain Spoken Dialogue System with Extensibility and Robustness against Speech Recognition Errors","authors":"Kazunori Komatani, Naoyuki Kanda, Mikio Nakano, K. Nakadai, H. Tsujino, T. Ogata, HIroshi G. Okuno","doi":"10.3115/1654595.1654598","DOIUrl":"https://doi.org/10.3115/1654595.1654598","url":null,"abstract":"We developed a multi-domain spoken dialogue system that can handle user requests across multiple domains. Such systems need to satisfy two requirements: extensibility and robustness against speech recognition errors. Extensibility is required to allow for the modification and addition of domains independent of other domains. Robustness against speech recognition errors is required because such errors are inevitable in speech recognition. However, the systems should still behave appropriately, even when their inputs are erroneous. Our system was constructed on an extensible architecture and is equipped with a robust and extensible domain selection method. Domain selection was based on three choices: (I) the previous domain, (II) the domain in which the speech recognition result can be accepted with the highest recognition score, and (III) other domains. With the third choice we newly introduced, our system can prevent dialogues from continuously being stuck in an erroneous domain. Our experimental results, obtained with 10 subjects, showed that our method reduced the domain selection errors by 18.3%, compared to a conventional method.","PeriodicalId":426429,"journal":{"name":"SIGDIAL Workshop","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125813670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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