SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A4SCQRYZ6" target="_blank" >RIV/00216208:11320/25:4SCQRYZ6 - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195927080&partnerID=40&md5=bcc7a99d0e7431964ec77f53346c3c86" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195927080&partnerID=40&md5=bcc7a99d0e7431964ec77f53346c3c86</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution

Original language description

Singleton mentions, i.e. entities mentioned only once in a text, are important to how humans understand discourse from a theoretical perspective. However previous attempts to incorporate their detection in end-to-end neural coreference resolution for English have been hampered by the lack of singleton mention spans in the OntoNotes benchmark. This paper addresses this limitation by combining predicted mentions from existing nested NER systems and features derived from OntoNotes syntax trees. With this approach, we create a near approximation of the OntoNotes dataset with all singleton mentions, achieving ∼94% recall on a sample of gold singletons. We then propose a two-step neural mention and coreference resolution system, named SPLICE, and compare its performance to the end-to-end approach in two scenarios: the OntoNotes test set and the out-of-domain (OOD) OntoGUM corpus. Results indicate that reconstructed singleton training yields results comparable to end-to-end systems for OntoNotes, while improving OOD stability (+1.1 avg. F1). We conduct error analysis for mention detection and delve into its impact on coreference clustering, revealing that precision improvements deliver more substantial benefits than increases in recall for resolving coreference chains. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Jt. Int. Conf. Comput. Linguist., Lang. Resour. Eval., LREC-COLING - Main Conf. Proc.

ISBN

978-249381410-4

ISSN

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e-ISSN

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Number of pages

11

Pages from-to

15191-15201

Publisher name

European Language Resources Association (ELRA)

Place of publication

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Event location

Torino, Italia

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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