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        <title>Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing</title>
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        <namePart type="given">Chunyang</namePart>
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            <title>Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges</title>
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            <namePart type="given">Jason</namePart>
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            <namePart type="given">Matthias</namePart>
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    <abstract>While sequence-to-sequence (seq2seq) models achieve state-of-the-art performance in many natural language processing tasks, they can be too slow for real-time applications. One performance bottleneck is predicting the most likely next token over a large vocabulary; methods to circumvent this bottleneck are a current research topic. We focus specifically on using seq2seq models for semantic parsing, where we observe that grammars often exist which specify valid formal representations of utterance semantics. By developing a generic approach for restricting the predictions of a seq2seq model to grammatically permissible continuations, we arrive at a widely applicable technique for speeding up semantic parsing. The technique leads to a 74% speed-up on an in-house dataset with a large vocabulary, compared to the same neural model without grammatical restrictions</abstract>
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%0 Conference Proceedings
%T Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing
%A Xiao, Chunyang
%A Teichmann, Christoph
%A Arkoudas, Konstantine
%Y Eisner, Jason
%Y Gallé, Matthias
%Y Heinz, Jeffrey
%Y Quattoni, Ariadna
%Y Rabusseau, Guillaume
%S Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence
%F xiao-etal-2019-grammatical
%X While sequence-to-sequence (seq2seq) models achieve state-of-the-art performance in many natural language processing tasks, they can be too slow for real-time applications. One performance bottleneck is predicting the most likely next token over a large vocabulary; methods to circumvent this bottleneck are a current research topic. We focus specifically on using seq2seq models for semantic parsing, where we observe that grammars often exist which specify valid formal representations of utterance semantics. By developing a generic approach for restricting the predictions of a seq2seq model to grammatically permissible continuations, we arrive at a widely applicable technique for speeding up semantic parsing. The technique leads to a 74% speed-up on an in-house dataset with a large vocabulary, compared to the same neural model without grammatical restrictions
%R 10.18653/v1/W19-3902
%U https://aclanthology.org/W19-3902/
%U https://doi.org/10.18653/v1/W19-3902
%P 14-23

Markdown (Informal)

[Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing](https://aclanthology.org/W19-3902/) (Xiao et al., ACL 2019)

• Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing (Xiao et al., ACL 2019)

ACL

• Chunyang Xiao, Christoph Teichmann, and Konstantine Arkoudas. 2019. Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing. In Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges, pages 14–23, Florence. Association for Computational Linguistics.