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        <title>Coreference Resolution through a seq2seq Transition-Based System</title>
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        <namePart type="given">Bernd</namePart>
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    <abstract>Most recent coreference resolution systems use search algorithms over possible spans to identify mentions and resolve coreference. We instead present a coreference resolution system that uses a text-to-text (seq2seq) paradigm to predict mentions and links jointly. We implement the coreference system as a transition system and use multilingual T5 as an underlying language model. We obtain state-of-the-art accuracy on the CoNLL-2012 datasets with 83.3 F1-score for English (a 2.3 higher F1-score than previous work [Dobrovolskii, 2021]) using only CoNLL data for training, 68.5 F1-score for Arabic (+4.1 higher than previous work), and 74.3 F1-score for Chinese (+5.3). In addition we use the SemEval-2010 data sets for experiments in the zero-shot setting, a few-shot setting, and supervised setting using all available training data. We obtain substantially higher zero-shot F1-scores for 3 out of 4 languages than previous approaches and significantly exceed previous supervised state-of-the-art results for all five tested languages. We provide the code and models as open source.1</abstract>
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        <date>2023</date>
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%0 Journal Article
%T Coreference Resolution through a seq2seq Transition-Based System
%A Bohnet, Bernd
%A Alberti, Chris
%A Collins, Michael
%J Transactions of the Association for Computational Linguistics
%D 2023
%V 11
%I MIT Press
%C Cambridge, MA
%F bohnet-etal-2023-coreference
%X Most recent coreference resolution systems use search algorithms over possible spans to identify mentions and resolve coreference. We instead present a coreference resolution system that uses a text-to-text (seq2seq) paradigm to predict mentions and links jointly. We implement the coreference system as a transition system and use multilingual T5 as an underlying language model. We obtain state-of-the-art accuracy on the CoNLL-2012 datasets with 83.3 F1-score for English (a 2.3 higher F1-score than previous work [Dobrovolskii, 2021]) using only CoNLL data for training, 68.5 F1-score for Arabic (+4.1 higher than previous work), and 74.3 F1-score for Chinese (+5.3). In addition we use the SemEval-2010 data sets for experiments in the zero-shot setting, a few-shot setting, and supervised setting using all available training data. We obtain substantially higher zero-shot F1-scores for 3 out of 4 languages than previous approaches and significantly exceed previous supervised state-of-the-art results for all five tested languages. We provide the code and models as open source.1
%R 10.1162/tacl_a_00543
%U https://aclanthology.org/2023.tacl-1.13/
%U https://doi.org/10.1162/tacl_a_00543
%P 212-226

Markdown (Informal)

[Coreference Resolution through a seq2seq Transition-Based System](https://aclanthology.org/2023.tacl-1.13/) (Bohnet et al., TACL 2023)

• Coreference Resolution through a seq2seq Transition-Based System (Bohnet et al., TACL 2023)

ACL

• Bernd Bohnet, Chris Alberti, and Michael Collins. 2023. Coreference Resolution through a seq2seq Transition-Based System. Transactions of the Association for Computational Linguistics, 11:212–226.