@inproceedings{01afe076223244c8986cc767d7b74e32,
title = "Jointly predicting predicates and arguments in neural semantic role labeling",
abstract = "Recent BIO-tagging-based neural semantic role labeling models are very high performing, but assume gold predicates as part of the input and cannot incorporate span-level features. We propose an end-to-end approach for jointly predicting all predicates, arguments spans, and the relations between them. The model makes independent decisions about what relationship, if any, holds between every possible word-span pair, and learns contextualized span representations that provide rich, shared input features for each decision. Experiments demonstrate that this approach sets a new state of the art on PropBank SRL without gold predicates.",
author = "Luheng He and Kenton Lee and Omer Levy and Luke Zettlemoyer",
note = "Publisher Copyright: c 2018 Association for Computational Linguistics; 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 ; Conference date: 15-07-2018 Through 20-07-2018",
year = "2018",
doi = "https://doi.org/10.18653/v1/p18-2058",
language = "الإنجليزيّة",
series = "ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)",
publisher = "Association for Computational Linguistics (ACL)",
pages = "364--369",
booktitle = "ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers)",
address = "الولايات المتّحدة",
}