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        <title>Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention</title>
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    <name type="personal">
        <namePart type="given">Pengcheng</namePart>
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        <namePart type="given">Hao</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Emmanouil</namePart>
        <namePart type="given">Antonios</namePart>
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            <title>Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies</title>
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    <abstract>We describe a span-level supervised attention loss that improves compositional generalization in semantic parsers. Our approach builds on existing losses that encourage attention maps in neural sequence-to-sequence models to imitate the output of classical word alignment algorithms. Where past work has used word-level alignments, we focus on spans; borrowing ideas from phrase-based machine translation, we align subtrees in semantic parses to spans of input sentences, and encourage neural attention mechanisms to mimic these alignments. This method improves the performance of transformers, RNNs, and structured decoders on three benchmarks of compositional generalization.</abstract>
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    <identifier type="doi">10.18653/v1/2021.naacl-main.225</identifier>
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        <date>2021-jun</date>
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            <start>2810</start>
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%0 Conference Proceedings
%T Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention
%A Yin, Pengcheng
%A Fang, Hao
%A Neubig, Graham
%A Pauls, Adam
%A Platanios, Emmanouil Antonios
%A Su, Yu
%A Thomson, Sam
%A Andreas, Jacob
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 jun
%I Association for Computational Linguistics
%C Online
%F yin-etal-2021-compositional
%X We describe a span-level supervised attention loss that improves compositional generalization in semantic parsers. Our approach builds on existing losses that encourage attention maps in neural sequence-to-sequence models to imitate the output of classical word alignment algorithms. Where past work has used word-level alignments, we focus on spans; borrowing ideas from phrase-based machine translation, we align subtrees in semantic parses to spans of input sentences, and encourage neural attention mechanisms to mimic these alignments. This method improves the performance of transformers, RNNs, and structured decoders on three benchmarks of compositional generalization.
%R 10.18653/v1/2021.naacl-main.225
%U https://aclanthology.org/2021.naacl-main.225
%U https://doi.org/10.18653/v1/2021.naacl-main.225
%P 2810-2823

Markdown (Informal)

[Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention](https://aclanthology.org/2021.naacl-main.225) (Yin et al., NAACL 2021)

• Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention (Yin et al., NAACL 2021)

ACL

• Pengcheng Yin, Hao Fang, Graham Neubig, Adam Pauls, Emmanouil Antonios Platanios, Yu Su, Sam Thomson, and Jacob Andreas. 2021. Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2810–2823, Online. Association for Computational Linguistics.