How to best use syntax in semantic role labelling

Yufei Wang, Mark Johnson, Stephen Wan, Yifang Sun, Wei Wang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

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Abstract

There are many different ways in which external information might be used in a NLP task. This paper investigates how external syntactic information can be used most effectively in the Semantic Role Labeling (SRL) task. We evaluate three different ways of encoding syntactic parses and three different ways of injecting them into a state-of-the-art neural ELMo-based SRL sequence labelling model. We show that using a constituency representation as input features improves performance the most, achieving a new state-of-the-art for non-ensemble SRL models on the in-domain CoNLL’05 and CoNLL’12 benchmarks.
Original languageEnglish
Title of host publicationProceedings of the 57th Annual Meeting of the Association for Computational Linguistics
EditorsAnna Korhonen, David Traum, Lluís Màrquez
Place of PublicationFlorence, Italy
PublisherAssociation for Computational Linguistics
Pages5338-5343
Number of pages6
ISBN (Print)9781950737482
DOIs
Publication statusPublished - Jul 2019
EventAnnual Meeting of the Association for Computational Linguistics (57th : 2019) - Florence, Italy
Duration: 28 Jul 20192 Aug 2019

Conference

ConferenceAnnual Meeting of the Association for Computational Linguistics (57th : 2019)
CountryItaly
CityFlorence
Period28/07/192/08/19

Bibliographical note

Copyright the Publisher 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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  • Cite this

    Wang, Y., Johnson, M., Wan, S., Sun, Y., & Wang, W. (2019). How to best use syntax in semantic role labelling. In A. Korhonen, D. Traum, & L. Màrquez (Eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 5338-5343). Florence, Italy: Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1529