Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution

TL;DR

This paper introduces the first model for resolving split-antecedent anaphors in coreference resolution, significantly improving performance by leveraging auxiliary data and transfer learning techniques.

Contribution

It presents a novel approach for unrestricted split-antecedent anaphora resolution, addressing data sparsity and demonstrating substantial performance gains.

Findings

Achieved 70% F1 score in lenient evaluation

Improved strict setting F1 score by 21 points

Utilized auxiliary corpora and transfer learning effectively

Abstract

Now 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.