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        <title>Sequence Labeling Parsing by Learning across Representations</title>
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        <namePart type="given">Michalina</namePart>
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        <namePart type="given">David</namePart>
        <namePart type="family">Vilares</namePart>
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    <name type="personal">
        <namePart type="given">Carlos</namePart>
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            <namePart type="given">Anna</namePart>
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            <namePart type="given">David</namePart>
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            <namePart type="given">Lluís</namePart>
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    <abstract>We use parsing as sequence labeling as a common framework to learn across constituency and dependency syntactic abstractions. To do so, we cast the problem as multitask learning (MTL). First, we show that adding a parsing paradigm as an auxiliary loss consistently improves the performance on the other paradigm. Secondly, we explore an MTL sequence labeling model that parses both representations, at almost no cost in terms of performance and speed. The results across the board show that on average MTL models with auxiliary losses for constituency parsing outperform single-task ones by 1.05 F1 points, and for dependency parsing by 0.62 UAS points.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1531</identifier>
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%0 Conference Proceedings
%T Sequence Labeling Parsing by Learning across Representations
%A Strzyz, Michalina
%A Vilares, David
%A Gómez-Rodríguez, Carlos
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F strzyz-etal-2019-sequence
%X We use parsing as sequence labeling as a common framework to learn across constituency and dependency syntactic abstractions. To do so, we cast the problem as multitask learning (MTL). First, we show that adding a parsing paradigm as an auxiliary loss consistently improves the performance on the other paradigm. Secondly, we explore an MTL sequence labeling model that parses both representations, at almost no cost in terms of performance and speed. The results across the board show that on average MTL models with auxiliary losses for constituency parsing outperform single-task ones by 1.05 F1 points, and for dependency parsing by 0.62 UAS points.
%R 10.18653/v1/P19-1531
%U https://aclanthology.org/P19-1531
%U https://doi.org/10.18653/v1/P19-1531
%P 5350-5357

Markdown (Informal)

[Sequence Labeling Parsing by Learning across Representations](https://aclanthology.org/P19-1531) (Strzyz et al., ACL 2019)

• Sequence Labeling Parsing by Learning across Representations (Strzyz et al., ACL 2019)

ACL

• Michalina Strzyz, David Vilares, and Carlos Gómez-Rodríguez. 2019. Sequence Labeling Parsing by Learning across Representations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5350–5357, Florence, Italy. Association for Computational Linguistics.