The Dialog State Tracking Challenge Series

Williams, Jason D.; Henderson, Matthew; Raux, Antoine; Thomson, Blaise; Black, Alan W.; Ramachandran, Deepak

doi:10.1609/aimag.v35i4.2558

Cited by 160 publications

(164 citation statements)

References 6 publications

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“…Once fully integrated and tuned, we plan to formally evaluate the performance of the algorithm on a portion of the SemEval 2016 AMR parsing shared task dataset (May, 2016) while measuring the impact of parsing errors on the end TMR quality. In addition, it would be interesting to empirically quantify the utility of our proposed pragmatic heuristics in the domain of a task-oriented dialogue such as the Dialogue State Tracking Challenge (Williams et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Fast Forward Through Opportunistic Incremental Meaning Representation Construction

Babkin

Ниренбург

2017

Proceedings of ACL 2017, Student Research Workshop

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One of the challenges semantic parsers face involves upstream errors originating from pre-processing modules such as ASR and syntactic parsers, which undermine the end result from the get go. We report the work in progress on a novel incremental semantic parsing algorithm that supports simultaneous application of independent heuristics and facilitates the construction of partial but potentially actionable meaning representations to overcome this problem. Our contribution to this point is mainly theoretical. In future work we intend to evaluate the algorithm as part of a dialogue understanding system on state of the art benchmarks.

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Section: Discussionmentioning

confidence: 99%

Fast Forward Through Opportunistic Incremental Meaning Representation Construction

Babkin

Ниренбург

2017

Proceedings of ACL 2017, Student Research Workshop

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“…Shared task regimes exist for some of these broad areas, such as the NIST speaker (NIST, 2016) and language recognition (NIST, 2015) tasks. The slot filling task also bears some similarities to spoken dialog system tasks, such as the Air Travel Information System (Mesnil et al, 2013) task and components of the Dialog State Tracking Challenge tasks (Williams et al, 2016). However, the setting of endangered language field recordings poses new and exciting challenges, while leveraging techniques developed for other languages in high resource settings.…”

Section: Intellectual Merit: Research Interest Inmentioning

confidence: 99%

STREAMLInED Challenges: Aligning Research Interests with Shared Tasks

Levow¹,

Bender²,

Littell³

et al. 2017

Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages

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While there have been significant improvements in speech and language processing, it remains difficult to bring these new tools to bear on challenges in endangered language documentation. We describe an effort to bridge this gap through Shared Task Evaluation Campaigns (STECs) by designing tasks that are compelling to speech and natural language processing researchers while addressing technical challenges in language documentation and exploiting growing archives of endangered language data. Based on discussions at a recent NSF-funded workshop, we present overarching design principles for these tasks: including realistic settings, diversity of data, accessibility of data and systems, and extensibility, that aim to ensure the utility of the resulting systems. Three planned tasks embodying these principles are highlighted: spanning audio processing, orthographic regularization, and automatic production of interlinear glossed text. The planned data and evaluation methodologies are also presented, motivating each task by its potential to accelerate the work of researchers and archivists working with endangered languages. Finally, we articulate the interest of the tasks to both speech and NLP researchers and speaker communities.

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“…The Belief Tracker merges the relational structure for the user's current utterance (produced by linguistic processing) with the relational structures from previous utterances to produce a coherent representation of the user's intent (Williams et al 2013;Yeh, Porter, and Barker 2005). Consider the illustrative dialogue in table 5.…”

Section: Belief Trackermentioning

confidence: 99%

An End‐to‐End Conversational Second‐Screen Application for TV Program Discovery

et al. 2015

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Articles FALL 2015 73T he recent explosion of content (such as movies, TV shows, sports, and others) available on television coupled with an increase use in mobile devices (that is, smartphones and tablets) has created significant interest in second-screen applications from both end users and content providers. Second screen applications are designed to run from mobile devices and to enhance the television viewing experience in numerous ways, one of which is helping end users effectively find and control content on television through spoken natural language (that is, conversational TV program discovery).Conversational TV program discovery applications have recently become available in the marketplace from select cable/satellite providers. However, these applications are limited. They support a predefined set of utterance types (for example, switch to , find a movie, and find a movie with ). Hence, end users must conform to

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The Dialog State Tracking Challenge Series

Cited by 160 publications

References 6 publications

Fast Forward Through Opportunistic Incremental Meaning Representation Construction

Fast Forward Through Opportunistic Incremental Meaning Representation Construction

STREAMLInED Challenges: Aligning Research Interests with Shared Tasks

An End‐to‐End Conversational Second‐Screen Application for TV Program Discovery

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