Search Results for author: Jayant Krishnamurthy

Found 17 papers, 4 papers with code

Value-Agnostic Conversational Semantic Parsing

no code implementations ACL 2021 Emmanouil Antonios Platanios, Adam Pauls, Subhro Roy, Yuchen Zhang, Alexander Kyte, Alan Guo, Sam Thomson, Jayant Krishnamurthy, Jason Wolfe, Jacob Andreas, Dan Klein

Conversational semantic parsers map user utterances to executable programs given dialogue histories composed of previous utterances, programs, and system responses.

Computational Efficiency Semantic Parsing

Learning a Neural Semantic Parser from User Feedback

no code implementations ACL 2017 Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, Luke Zettlemoyer

We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention.

SQL Parsing

Probabilistic Neural Programs

no code implementations2 Dec 2016 Kenton W. Murray, Jayant Krishnamurthy

We present probabilistic neural programs, a framework for program induction that permits flexible specification of both a computational model and inference algorithm while simultaneously enabling the use of deep neural networks.

Program induction Question Answering

Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge

1 code implementation12 Jul 2016 Matt Gardner, Jayant Krishnamurthy

However, all prior approaches to open vocabulary semantic parsing replace a formal KB with textual information, making no use of the KB in their models.

Question Answering Semantic Parsing

Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary

no code implementations TACL 2015 Jayant Krishnamurthy, Tom M. Mitchell

Crucially, our approach uses an open predicate vocabulary, enabling it to produce denotations for phrases such as {``}Republican front-runner from Texas{''} whose semantics cannot be represented using the Freebase schema.

Coreference Resolution Open Information Extraction +3

Jointly Learning to Parse and Perceive: Connecting Natural Language to the Physical World

no code implementations TACL 2013 Jayant Krishnamurthy, Thomas Kollar

LSP learns physical representations for both categorical ({``}blue,{''} {``}mug{''}) and relational ({``}on{''}) language, and also learns to compose these representations to produce the referents of entire statements.

Language Acquisition Question Answering +1

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