{"title":"对话状态跟踪挑战系列:回顾","authors":"J. Williams, Antoine Raux, Matthew Henderson","doi":"10.5087/dad.2016.301","DOIUrl":null,"url":null,"abstract":"In a spoken dialog system, dialog state tracking refers to the task of correctly inferring the state of the conversation -- such as the user's goal -- given all of the dialog history up to that turn. Dialog state tracking is crucial to the success of a dialog system, yet until recently there were no common resources, hampering progress. The Dialog State Tracking Challenge series of 3 tasks introduced the first shared testbed and evaluation metrics for dialog state tracking, and has underpinned three key advances in dialog state tracking: the move from generative to discriminative models; the adoption of discriminative sequential techniques; and the incorporation of the speech recognition results directly into the dialog state tracker. This paper reviews this research area, covering both the challenge tasks themselves and summarizing the work they have enabled.","PeriodicalId":37604,"journal":{"name":"Dialogue and Discourse","volume":"21 1","pages":"4-33"},"PeriodicalIF":0.0000,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"209","resultStr":"{\"title\":\"The Dialog State Tracking Challenge Series: A Review\",\"authors\":\"J. Williams, Antoine Raux, Matthew Henderson\",\"doi\":\"10.5087/dad.2016.301\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In a spoken dialog system, dialog state tracking refers to the task of correctly inferring the state of the conversation -- such as the user's goal -- given all of the dialog history up to that turn. Dialog state tracking is crucial to the success of a dialog system, yet until recently there were no common resources, hampering progress. The Dialog State Tracking Challenge series of 3 tasks introduced the first shared testbed and evaluation metrics for dialog state tracking, and has underpinned three key advances in dialog state tracking: the move from generative to discriminative models; the adoption of discriminative sequential techniques; and the incorporation of the speech recognition results directly into the dialog state tracker. This paper reviews this research area, covering both the challenge tasks themselves and summarizing the work they have enabled.\",\"PeriodicalId\":37604,\"journal\":{\"name\":\"Dialogue and Discourse\",\"volume\":\"21 1\",\"pages\":\"4-33\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"209\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Dialogue and Discourse\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5087/dad.2016.301\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Arts and Humanities\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Dialogue and Discourse","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5087/dad.2016.301","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Arts and Humanities","Score":null,"Total":0}
The Dialog State Tracking Challenge Series: A Review
In a spoken dialog system, dialog state tracking refers to the task of correctly inferring the state of the conversation -- such as the user's goal -- given all of the dialog history up to that turn. Dialog state tracking is crucial to the success of a dialog system, yet until recently there were no common resources, hampering progress. The Dialog State Tracking Challenge series of 3 tasks introduced the first shared testbed and evaluation metrics for dialog state tracking, and has underpinned three key advances in dialog state tracking: the move from generative to discriminative models; the adoption of discriminative sequential techniques; and the incorporation of the speech recognition results directly into the dialog state tracker. This paper reviews this research area, covering both the challenge tasks themselves and summarizing the work they have enabled.
期刊介绍：
D&D seeks previously unpublished, high quality articles on the analysis of discourse and dialogue that contain -experimental and/or theoretical studies related to the construction, representation, and maintenance of (linguistic) context -linguistic analysis of phenomena characteristic of discourse and/or dialogue (including, but not limited to: reference and anaphora, presupposition and accommodation, topicality and salience, implicature, ---discourse structure and rhetorical relations, discourse markers and particles, the semantics and -pragmatics of dialogue acts, questions, imperatives, non-sentential utterances, intonation, and meta--communicative phenomena such as repair and grounding) -experimental and/or theoretical studies of agents'' information states and their dynamics in conversational interaction -new analytical frameworks that advance theoretical studies of discourse and dialogue -research on systems performing coreference resolution, discourse structure parsing, event and temporal -structure, and reference resolution in multimodal communication -experimental and/or theoretical results yielding new insight into non-linguistic interaction in -communication -work on natural language understanding (including spoken language understanding), dialogue management, -reasoning, and natural language generation (including text-to-speech) in dialogue systems -work related to the design and engineering of dialogue systems (including, but not limited to: -evaluation, usability design and testing, rapid application deployment, embodied agents, affect detection, -mixed-initiative, adaptation, and user modeling). -extremely well-written surveys of existing work. Highest priority is given to research reports that are specifically written for a multidisciplinary audience. The audience is primarily researchers on discourse and dialogue and its associated fields, including computer scientists, linguists, psychologists, philosophers, roboticists, sociologists.
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