Vijay Saraswat


2021

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KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction
Abhishek Nadgeri | Anson Bastos | Kuldeep Singh | Isaiah Onando Mulang’ | Johannes Hoffart | Saeedeh Shekarpour | Vijay Saraswat
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2019

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Learning an Executable Neural Semantic Parser
Jianpeng Cheng | Siva Reddy | Vijay Saraswat | Mirella Lapata
Computational Linguistics, Volume 45, Issue 1 - March 2019

This article describes a neural semantic parser that maps natural language utterances onto logical forms that can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generates tree-structured logical forms with a transition-based approach, combining a generic tree-generation algorithm with domain-general grammar defined by the logical language. The generation process is modeled by structured recurrent neural networks, which provide a rich encoding of the sentential context and generation history for making predictions. To tackle mismatches between natural language and logical form tokens, various attention mechanisms are explored. Finally, we consider different training settings for the neural semantic parser, including fully supervised training where annotated logical forms are given, weakly supervised training where denotations are provided, and distant supervision where only unlabeled sentences and a knowledge base are available. Experiments across a wide range of data sets demonstrate the effectiveness of our parser.

2017

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A Probabilistic Generative Grammar for Semantic Parsing
Abulhair Saparov | Vijay Saraswat | Tom Mitchell
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

We present a generative model of natural language sentences and demonstrate its application to semantic parsing. In the generative process, a logical form sampled from a prior, and conditioned on this logical form, a grammar probabilistically generates the output sentence. Grammar induction using MCMC is applied to learn the grammar given a set of labeled sentences with corresponding logical forms. We develop a semantic parser that finds the logical form with the highest posterior probability exactly. We obtain strong results on the GeoQuery dataset and achieve state-of-the-art F1 on Jobs.

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Learning Structured Natural Language Representations for Semantic Parsing
Jianpeng Cheng | Siva Reddy | Vijay Saraswat | Mirella Lapata
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We introduce a neural semantic parser which is interpretable and scalable. Our model converts natural language utterances to intermediate, domain-general natural language representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We achieve the state of the art on SPADES and GRAPHQUESTIONS and obtain competitive results on GEOQUERY and WEBQUESTIONS. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones.

1995

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The Semantics of Resource Sharing in Lexical-Functional Grammar
Andrew Kehler | Mary Dalrymple | John Lamping | Vijay Saraswat
Seventh Conference of the European Chapter of the Association for Computational Linguistics

1993

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LFG Semantics via Constraints
Mary Dalrymple | John Lamping | Vijay Saraswat
Sixth Conference of the European Chapter of the Association for Computational Linguistics