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        <title>Memory-bounded Neural Incremental Parsing for Psycholinguistic Prediction</title>
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        <namePart type="given">Lifeng</namePart>
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            <title>Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies</title>
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            <namePart type="given">Gosse</namePart>
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            <namePart type="given">Yuji</namePart>
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        <name type="personal">
            <namePart type="given">Weiwei</namePart>
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    <abstract>Syntactic surprisal has been shown to have an effect on human sentence processing, and can be predicted from prefix probabilities of generative incremental parsers. Recent state-of-the-art incremental generative neural parsers are able to produce accurate parses and surprisal values but have unbounded stack memory, which may be used by the neural parser to maintain explicit in-order representations of all previously parsed words, inconsistent with results of human memory experiments. In contrast, humans seem to have a bounded working memory, demonstrated by inhibited performance on word recall in multi-clause sentences (Bransford and Franks, 1971), and on center-embedded sentences (Miller and Isard,1964). Bounded statistical parsers exist, but are less accurate than neural parsers in predict-ing reading times. This paper describes a neural incremental generative parser that is able to provide accurate surprisal estimates and can be constrained to use a bounded stack. Results show that the accuracy gains of neural parsers can be reliably extended to psycholinguistic modeling without risk of distortion due to un-bounded working memory.</abstract>
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%0 Conference Proceedings
%T Memory-bounded Neural Incremental Parsing for Psycholinguistic Prediction
%A Jin, Lifeng
%A Schuler, William
%Y Bouma, Gosse
%Y Matsumoto, Yuji
%Y Oepen, Stephan
%Y Sagae, Kenji
%Y Seddah, Djamé
%Y Sun, Weiwei
%Y Søgaard, Anders
%Y Tsarfaty, Reut
%Y Zeman, Dan
%S Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F jin-schuler-2020-memory
%X Syntactic surprisal has been shown to have an effect on human sentence processing, and can be predicted from prefix probabilities of generative incremental parsers. Recent state-of-the-art incremental generative neural parsers are able to produce accurate parses and surprisal values but have unbounded stack memory, which may be used by the neural parser to maintain explicit in-order representations of all previously parsed words, inconsistent with results of human memory experiments. In contrast, humans seem to have a bounded working memory, demonstrated by inhibited performance on word recall in multi-clause sentences (Bransford and Franks, 1971), and on center-embedded sentences (Miller and Isard,1964). Bounded statistical parsers exist, but are less accurate than neural parsers in predict-ing reading times. This paper describes a neural incremental generative parser that is able to provide accurate surprisal estimates and can be constrained to use a bounded stack. Results show that the accuracy gains of neural parsers can be reliably extended to psycholinguistic modeling without risk of distortion due to un-bounded working memory.
%R 10.18653/v1/2020.iwpt-1.6
%U https://aclanthology.org/2020.iwpt-1.6
%U https://doi.org/10.18653/v1/2020.iwpt-1.6
%P 48-61

Markdown (Informal)

[Memory-bounded Neural Incremental Parsing for Psycholinguistic Prediction](https://aclanthology.org/2020.iwpt-1.6) (Jin & Schuler, IWPT 2020)

• Memory-bounded Neural Incremental Parsing for Psycholinguistic Prediction (Jin & Schuler, IWPT 2020)

ACL

• Lifeng Jin and William Schuler. 2020. Memory-bounded Neural Incremental Parsing for Psycholinguistic Prediction. In Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies, pages 48–61, Online. Association for Computational Linguistics.