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            <namePart type="given">Zornitsa</namePart>
            <namePart type="family">Kozareva</namePart>
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            <namePart type="given">Sujith</namePart>
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            <namePart type="given">Andreas</namePart>
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    <abstract>The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depth-first traversal. In this work, we propose an alternative approach: a Semi-autoregressive Bottom-up Parser (SmBoP) that constructs at decoding step t the top-K sub-trees of height łeq t. Our parser enjoys several benefits compared to top-down autoregressive parsing. From an efficiency perspective, bottom-up parsing allows to decode all sub-trees of a certain height in parallel, leading to logarithmic runtime complexity rather than linear. From a modeling perspective, a bottom-up parser learns representations for meaningful semantic sub-programs at each step, rather than for semantically-vacuous partial trees. We apply SmBoP on Spider, a challenging zero-shot semantic parsing benchmark, and show that SmBoP leads to a 2.2x speed-up in decoding time and a ~5x speed-up in training time, compared to a semantic parser that uses autoregressive decoding. SmBoP obtains 71.1 denotation accuracy on Spider, establishing a new state-of-the-art, and 69.5 exact match, comparable to the 69.6 exact match of the autoregressive RAT-SQL+Grappa.</abstract>
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%0 Conference Proceedings
%T SmBoP: Semi-autoregressive Bottom-up Semantic Parsing
%A Rubin, Ohad
%A Berant, Jonathan
%Y Kozareva, Zornitsa
%Y Ravi, Sujith
%Y Vlachos, Andreas
%Y Agrawal, Priyanka
%Y Martins, André
%S Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F rubin-berant-2021-smbop-semi
%X The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depth-first traversal. In this work, we propose an alternative approach: a Semi-autoregressive Bottom-up Parser (SmBoP) that constructs at decoding step t the top-K sub-trees of height łeq t. Our parser enjoys several benefits compared to top-down autoregressive parsing. From an efficiency perspective, bottom-up parsing allows to decode all sub-trees of a certain height in parallel, leading to logarithmic runtime complexity rather than linear. From a modeling perspective, a bottom-up parser learns representations for meaningful semantic sub-programs at each step, rather than for semantically-vacuous partial trees. We apply SmBoP on Spider, a challenging zero-shot semantic parsing benchmark, and show that SmBoP leads to a 2.2x speed-up in decoding time and a ~5x speed-up in training time, compared to a semantic parser that uses autoregressive decoding. SmBoP obtains 71.1 denotation accuracy on Spider, establishing a new state-of-the-art, and 69.5 exact match, comparable to the 69.6 exact match of the autoregressive RAT-SQL+Grappa.
%R 10.18653/v1/2021.spnlp-1.2
%U https://aclanthology.org/2021.spnlp-1.2
%U https://doi.org/10.18653/v1/2021.spnlp-1.2
%P 12-21

Markdown (Informal)

[SmBoP: Semi-autoregressive Bottom-up Semantic Parsing](https://aclanthology.org/2021.spnlp-1.2) (Rubin & Berant, spnlp 2021)

• SmBoP: Semi-autoregressive Bottom-up Semantic Parsing (Rubin & Berant, spnlp 2021)

ACL

• Ohad Rubin and Jonathan Berant. 2021. SmBoP: Semi-autoregressive Bottom-up Semantic Parsing. In Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021), pages 12–21, Online. Association for Computational Linguistics.