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2021 | OriginalPaper | Buchkapitel

Coreference Resolution: Are the Eliminated Spans Totally Worthless?

verfasst von : Xin Tan, Longyin Zhang, Guodong Zhou

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

Up to date, various neural-based methods have been proposed for joint mention span detection and coreference resolution. However, existing studies on coreference resolution mainly depend on mention representations, while the rest spans in the text are largely ignored and directly eliminated. In this paper, we aim at investigating whether those eliminated spans are totally worthless, or to what extent they can help improve the performance of coreference resolution. To achieve this goal, we propose to refine the representation of mentions with global spans including these eliminated ones leveraged. On this basis, we further introduce an additional loss term in this work to encourage the diversity between different entity clusters. Experimental results on the document-level CoNLL-2012 Shared Task English dataset show that the eliminated spans are indeed useful and our proposed approaches show promising results in coreference resolution.

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Nächstes Kapitel Chinese Macro Discourse Parsing on Dependency Graph Convolutional Network

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Titel: Coreference Resolution: Are the Eliminated Spans Totally Worthless?
verfasst von: Xin Tan
Longyin Zhang
Guodong Zhou
Verlag: Springer International Publishing
Buch: Natural Language Processing and Chinese Computing
Print ISBN: 978-3-030-88479-6

Electronic ISBN: 978-3-030-88480-2

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-88480-2_1

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