Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution
3829Authors
nan
Abstract
AbstractNow that the performance of coreference resolvers on the simpler forms of anaphoric reference has greatly improved, more attention is devoted to more complex aspects of anaphora. One limitation of virtually all coreference resolution models is the focus on single-antecedent anaphors. Plural anaphors with multiple antecedents-so-called split-antecedent anaphors (as in John met Mary. They went to the movies) have not been widely studied, because they are not annotated in ONTONOTES and are relatively infrequent in other corpora. In this paper, we introduce the first model for unrestricted resolution of split-antecedent anaphors. We start with a strong baseline enhanced by BERT embeddings, and show that we can substantially improve its performance by addressing the sparsity issue. To do this, we experiment with auxiliary corpora where split-antecedent anaphors were annotated by the crowd, and with transfer learning models using element-of bridging references and single-antecedent coreference as auxiliary tasks. Evaluation on the gold annotated ARRAU corpus shows that the out best model uses a combination of three auxiliary corpora achieved F1 scores of 70% and 43.6% when evaluated in a lenient and strict setting, respectively, i.e., 11 and 21 percentage points gain when compared with our baseline.
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Proceedings of the 28th International Conference on Computational Linguistics, pages 6113–6125 Barcelona, Spain (Online), December 8-13, 2020 6113 Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution Juntao Yu1, Nafise Sadat Moosavi2, Silviu Paun1, Massimo Poesio1 1Queen Mary University of London 2UKP Lab, Technische Universit¨at Darmstadt 1{juntao.yu, s.paun, m.poesio}@qmul.ac.uk 2moosavi@ukp.informatik.tu-darmstadt.de Abstract Now that the performance of coreference resolvers on the simpler forms of anaphoric refer- ence has greatly improved, more attention is devoted to more complex aspects of anaphora. One limitation of virtually all coreference resolution models is the focus on single-antecedent anaphors. Plural anaphors with multiple antecedents–so-called split-antecedent anaphors (as in John met Mary. They went to the movies)–have not been widely studied, because they are not an- notated in ONTONOTES and are relatively infrequent in other corpora. In this paper, we introduce the first model for unrestricted resolution of split-antecedent anaphors. We start with a strong baseline enhanced by BERT embeddings, and show that we can substantially improve its perfor- mance by addressing the sparsity issue. To do this, we experiment with auxiliary corpora where split-antecedent anaphors were annotated by the crowd, and with transfer learning models using element-of bridging references and single-antecedent coreference as auxiliary tasks. Evaluation on the gold annotated ARRAU corpus shows that the out best model uses a combination of three auxiliary corpora achieved F1 scores of 70% and 43.6% when evaluated in a lenient and strict setting, respectively, i.e., 11 and 21 percentage points gain when compared with our baseline.1 1 Introduction (Identity) anaphora resolution (coreference) is the linguistic task of linking nominal expressions (men- tions) to entities in the discourse, so that mentions representing the same entity are grouped together in a ‘coreference chain’ (Poesio et al., 2016). As the performance of coreference models has substantially improved (Clark and Manning, 2015; Clark and Manning, 2016; Lee et al., 2017; Lee et al., 2018; Kantor and Globerson, 2019) in recent years, more attention is being devoted to more complex aspects of anaphoric reference– from the pronouns that require commonsense knowledge for their resolution studied in the Winograd Schema Challenge (Rahman and Ng, 2012; Peng et al., 2015) to pronouns that cannot be resolved purely on the basis of gender (Webster et al., 2018). Another limitation of state of the art systems is the assumption that anaphors can only have one antecedent. Plural anaphors with multiple antecedents (split antecedent anaphors) are not widely studied– in fact, such anaphors are not anno- tated in the most widely used coreference corpus, ONTONOTES (Pradhan et al., 2012). In ONTONOTES we find annotated cases of plural reference to plural antecedents, as in (1), or cases in which singular antecedents are conjoined so that a mention can be introduced for the conjunction, as in (2). However, it is also possible to refer plurally to antecedents introduced by separate noun phrases, as in (3) or (4) (Eschenbach et al., 1989; Kamp and Reyle, 1993); such cases are not annotated in ONTONOTES. (1) The Jonesesi went to the park. Theyi had a good …
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AbstractNow that the performance of coreference resolvers on the simpler forms of anaphoric reference has greatly improved, more attention is devoted to more complex aspects of anaphora. One limitation of virtually all coreference resolution models is the focus on single-antecedent anaphors. Plural anaphors with multiple antecedents-so-called split-antecedent anaphors (as in John met Mary. They went to the movies) have not been widely studied, because they are not annotated in ONTONOTES and are relatively infrequent in other corpora. In this paper, we introduce the first model for unrestricted resolution of split-antecedent anaphors. We start with a strong baseline enhanced by BERT embeddings, and show that we can substantially improve its performance by addressing the sparsity issue. To do this, we experiment with auxiliary corpora where split-antecedent anaphors were annotated by the crowd, and with transfer learning models using element-of bridging references and single-antecedent coreference as auxiliary tasks. Evaluation on the gold annotated ARRAU corpus shows that the out best model uses a combination of three auxiliary corpora achieved F1 scores of 70% and 43.6% when evaluated in a lenient and strict setting, respectively, i.e., 11 and 21 percentage points gain when compared with our baseline.
Publication Details
- Paper ID:
- 3829
- Year:
- 2020
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan