ÚFAL CorPipe at CRAC 2023: Larger Context Improves Multilingual Coreference Resolution

Fuente: arXiv
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Main Author: Straka, Milan
Format: Preprint
Published: 2023
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author Straka, Milan
author_facet Straka, Milan
contents We present CorPipe, the winning entry to the CRAC 2023 Shared Task on Multilingual Coreference Resolution. Our system is an improved version of our earlier multilingual coreference pipeline, and it surpasses other participants by a large margin of 4.5 percent points. CorPipe first performs mention detection, followed by coreference linking via an antecedent-maximization approach on the retrieved spans. Both tasks are trained jointly on all available corpora using a shared pretrained language model. Our main improvements comprise inputs larger than 512 subwords and changing the mention decoding to support ensembling. The source code is available at https://github.com/ufal/crac2023-corpipe.
format Preprint
id arxiv_https___arxiv_org_abs_2311_14391
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle ÚFAL CorPipe at CRAC 2023: Larger Context Improves Multilingual Coreference Resolution
Straka, Milan
Computation and Language
We present CorPipe, the winning entry to the CRAC 2023 Shared Task on Multilingual Coreference Resolution. Our system is an improved version of our earlier multilingual coreference pipeline, and it surpasses other participants by a large margin of 4.5 percent points. CorPipe first performs mention detection, followed by coreference linking via an antecedent-maximization approach on the retrieved spans. Both tasks are trained jointly on all available corpora using a shared pretrained language model. Our main improvements comprise inputs larger than 512 subwords and changing the mention decoding to support ensembling. The source code is available at https://github.com/ufal/crac2023-corpipe.
title ÚFAL CorPipe at CRAC 2023: Larger Context Improves Multilingual Coreference Resolution
topic Computation and Language
url https://arxiv.org/abs/2311.14391