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        <title>PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models</title>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Marie-Francine</namePart>
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            <namePart type="given">Xuanjing</namePart>
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            <namePart type="given">Lucia</namePart>
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    <abstract>Large pre-trained language models for textual data have an unconstrained output space; at each decoding step, they can produce any of 10,000s of sub-word tokens. When fine-tuned to target constrained formal languages like SQL, these models often generate invalid code, rendering it unusable. We propose PICARD (code available at https://github.com/ElementAI/picard), a method for constraining auto-regressive decoders of language models through incremental parsing. PICARD helps to find valid output sequences by rejecting inadmissible tokens at each decoding step. On the challenging Spider and CoSQL text-to-SQL translation tasks, we show that PICARD transforms fine-tuned T5 models with passable performance into state-of-the-art solutions.</abstract>
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%0 Conference Proceedings
%T PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
%A Scholak, Torsten
%A Schucher, Nathan
%A Bahdanau, Dzmitry
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F scholak-etal-2021-picard
%X Large pre-trained language models for textual data have an unconstrained output space; at each decoding step, they can produce any of 10,000s of sub-word tokens. When fine-tuned to target constrained formal languages like SQL, these models often generate invalid code, rendering it unusable. We propose PICARD (code available at https://github.com/ElementAI/picard), a method for constraining auto-regressive decoders of language models through incremental parsing. PICARD helps to find valid output sequences by rejecting inadmissible tokens at each decoding step. On the challenging Spider and CoSQL text-to-SQL translation tasks, we show that PICARD transforms fine-tuned T5 models with passable performance into state-of-the-art solutions.
%R 10.18653/v1/2021.emnlp-main.779
%U https://aclanthology.org/2021.emnlp-main.779
%U https://doi.org/10.18653/v1/2021.emnlp-main.779
%P 9895-9901

Markdown (Informal)

[PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://aclanthology.org/2021.emnlp-main.779) (Scholak et al., EMNLP 2021)

• PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models (Scholak et al., EMNLP 2021)

ACL

• Torsten Scholak, Nathan Schucher, and Dzmitry Bahdanau. 2021. PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9895–9901, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.