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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.
|Title of host publication||Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics|
|Editors||Anna Korhonen, David Traum, Lluís Màrquez|
|Place of Publication||Florence, Italy|
|Publisher||Association for Computational Linguistics|
|Number of pages||6|
|Publication status||Published - Jul 2019|
|Event||Annual Meeting of the Association for Computational Linguistics (57th : 2019) - Florence, Italy|
Duration: 28 Jul 2019 → 2 Aug 2019
|Name||ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference|
|Conference||Annual Meeting of the Association for Computational Linguistics (57th : 2019)|
|Period||28/07/19 → 2/08/19|
Bibliographical noteCopyright 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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