Author pages are created from data sourced from our academic publisher partnerships and public sources.
- Publications
- Influence
Scaling Semantic Parsers with On-the-Fly Ontology Matching
- T. Kwiatkowski, Eunsol Choi, Yoav Artzi, Luke Zettlemoyer
- Computer Science
- EMNLP
- 1 October 2013
We consider the challenge of learning semantic parsers that scale to large, open-domain problems, such as question answering with Freebase. In such settings, the sentences cover a wide variety of… Expand
Natural Questions: A Benchmark for Question Answering Research
- T. Kwiatkowski, J. Palomaki, +15 authors Slav Petrov
- Computer Science
- Transactions of the Association for Computational…
- 2 August 2019
We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a… Expand
Inducing Probabilistic CCG Grammars from Logical Form with Higher-Order Unification
- T. Kwiatkowski, Luke Zettlemoyer, S. Goldwater, Mark Steedman
- Computer Science
- EMNLP
- 9 October 2010
This paper addresses the problem of learning to map sentences to logical form, given training data consisting of natural language sentences paired with logical representations of their meaning.… Expand
Association for Computational Linguistics: Human Language Technologies
- H. Chinaei, M. Dreyer, +11 authors F. Yang
- Computer Science
- 2011
- 325
- 22
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
- Christopher Clark, Kenton Lee, Ming-Wei Chang, T. Kwiatkowski, M. Collins, Kristina Toutanova
- Computer Science
- NAACL-HLT
- 24 May 2019
In this paper we study yes/no questions that are naturally occurring --- meaning that they are generated in unprompted and unconstrained settings. We build a reading comprehension dataset, BoolQ, of… Expand
Lexical Generalization in CCG Grammar Induction for Semantic Parsing
- T. Kwiatkowski, Luke Zettlemoyer, S. Goldwater, Mark Steedman
- Computer Science
- EMNLP
- 27 July 2011
We consider the problem of learning factored probabilistic CCG grammars for semantic parsing from data containing sentences paired with logical-form meaning representations. Traditional CCG lexicons… Expand
Learning Recurrent Span Representations for Extractive Question Answering
- Kenton Lee, T. Kwiatkowski, Ankur P. Parikh, Dipanjan Das
- Computer Science
- ArXiv
- 4 November 2016
The reading comprehension task, that asks questions about a given evidence document, is a central problem in natural language understanding. Recent formulations of this task have typically focused on… Expand
Matching the Blanks: Distributional Similarity for Relation Learning
- Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, T. Kwiatkowski
- Computer Science
- ACL
- 7 June 2019
General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction. Efforts have been made to build general purpose extractors that represent… Expand
Early stroke treatment associated with better outcome: the ninds rt-pa stroke study
- J. Marler, B. Tilley, +10 authors T. Kwiatkowski
- Medicine
- Neurology
- 12 December 2000
Background The National Institute of Neurological Disorders and Stroke (NINDS) rt-PA Stroke Study showed a similar percentage of intracranial hemorrhage and good outcome in patients 3 months after… Expand
Transforming Dependency Structures to Logical Forms for Semantic Parsing
- Siva Reddy, Oscar Täckström, +4 authors Mirella Lapata
- Computer Science
- Transactions of the Association for Computational…
- 25 April 2016
The strongly typed syntax of grammar formalisms such as CCG, TAG, LFG and HPSG offers a synchronous framework for deriving syntactic structures and semantic logical forms. In contrast—partly due to… Expand