David Burkett


2020

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Task-Oriented Dialogue as Dataflow Synthesis
Jacob Andreas | John Bufe | David Burkett | Charles Chen | Josh Clausman | Jean Crawford | Kate Crim | Jordan DeLoach | Leah Dorner | Jason Eisner | Hao Fang | Alan Guo | David Hall | Kristin Hayes | Kellie Hill | Diana Ho | Wendy Iwaszuk | Smriti Jha | Dan Klein | Jayant Krishnamurthy | Theo Lanman | Percy Liang | Christopher H. Lin | Ilya Lintsbakh | Andy McGovern | Aleksandr Nisnevich | Adam Pauls | Dmitrij Petters | Brent Read | Dan Roth | Subhro Roy | Jesse Rusak | Beth Short | Div Slomin | Ben Snyder | Stephon Striplin | Yu Su | Zachary Tellman | Sam Thomson | Andrei Vorobev | Izabela Witoszko | Jason Wolfe | Abby Wray | Yuchen Zhang | Alexander Zotov
Transactions of the Association for Computational Linguistics, Volume 8

We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph. A dialogue agent maps each user utterance to a program that extends this graph. Programs include metacomputation operators for reference and revision that reuse dataflow fragments from previous turns. Our graph-based state enables the expression and manipulation of complex user intents, and explicit metacomputation makes these intents easier for learned models to predict. We introduce a new dataset, SMCalFlow, featuring complex dialogues about events, weather, places, and people. Experiments show that dataflow graphs and metacomputation substantially improve representability and predictability in these natural dialogues. Additional experiments on the MultiWOZ dataset show that our dataflow representation enables an otherwise off-the-shelf sequence-to-sequence model to match the best existing task-specific state tracking model. The SMCalFlow dataset, code for replicating experiments, and a public leaderboard are available at https://www.microsoft.com/en-us/research/project/dataflow-based-dialogue-semantic-machines.

2014

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Structured Learning for Taxonomy Induction with Belief Propagation
Mohit Bansal | David Burkett | Gerard de Melo | Dan Klein
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Variational Inference for Structured NLP Models
David Burkett | Dan Klein
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Tutorials)

2012

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Transforming Trees to Improve Syntactic Convergence
David Burkett | Dan Klein
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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An Empirical Investigation of Statistical Significance in NLP
Taylor Berg-Kirkpatrick | David Burkett | Dan Klein
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Fast Inference in Phrase Extraction Models with Belief Propagation
David Burkett | Dan Klein
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Variational Inference for Structured NLP Models
David Burkett | Dan Klein
Tutorial Abstracts at the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2011

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Mention Detection: Heuristics for the OntoNotes annotations
Jonathan K. Kummerfeld | Mohit Bansal | David Burkett | Dan Klein
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task

2010

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Joint Parsing and Alignment with Weakly Synchronized Grammars
David Burkett | John Blitzer | Dan Klein
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Learning Better Monolingual Models with Unannotated Bilingual Text
David Burkett | Slav Petrov | John Blitzer | Dan Klein
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

2008

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Two Languages are Better than One (for Syntactic Parsing)
David Burkett | Dan Klein
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing