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.

该论文介绍了第一个用于无限制解决 split-antecedent anaphors 的模型，使用 BERT 嵌入及辅助语料库进行了实验，并在金准 ARRAU 语料库上取得了显著的性能提升。

自由化复数：不受限制的分裂先行语回指的解析

Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution

Gender bias has been found in existing coreference resolvers. In order to
eliminate gender bias, a gender-balanced dataset Gendered Ambiguous Pronouns
(GAP) has been released and the best baseline model achieves only 66.9% F1.
Bidirectional Encoder Representations from Transformers (BERT) has broken
several NLP task records and can be used on GAP dataset. However, fine-tune
BERT on a specific task is computationally expensive. In this paper, we propose
an end-to-end resolver by combining pre-trained BERT with Relational Graph
Convolutional Network (R-GCN). R-GCN is used for digesting structural syntactic
information and learning better task-specific embeddings. Empirical results
demonstrate that, under explicit syntactic supervision and without the need to
fine tune BERT, R-GCN's embeddings outperform the original BERT embeddings on
the coreference task. Our work significantly improves the snippet-context
baseline F1 score on GAP dataset from 66.9% to 80.3%. We participated in the
2019 GAP Coreference Shared Task, and our codes are available online.

本文在结构句法的监督下，通过将预训练 BERT 与关系图卷积网络 (R-GCN) 相结合，提出了一种端到端的关联分辨器，R-GCN 的嵌入在指代任务上优于 BERT 的原始嵌入，从而将 GAP 数据集上的片段 - 上下文基线 F1 分数显著提高了 13.4 个百分点， 解决了现有的指代分辨器存在的性别偏差问题。

重新审视句法结构：基于关系图卷积网络的性别歧视指代消解

Look Again at the Syntax: Relational Graph Convolutional Network for  Gendered Ambiguous Pronoun Resolution

Lexical features are a major source of information in state-of-the-art
coreference resolvers. Lexical features implicitly model some of the linguistic
phenomena at a fine granularity level. They are especially useful for
representing the context of mentions. In this paper we investigate a drawback
of using many lexical features in state-of-the-art coreference resolvers. We
show that if coreference resolvers mainly rely on lexical features, they can
hardly generalize to unseen domains. Furthermore, we show that the current
coreference resolution evaluation is clearly flawed by only evaluating on a
specific split of a specific dataset in which there is a notable overlap
between the training, development and test sets.

本文研究了在先进的共指消解器中使用大量词汇特征的一个缺点，即如果核心语义分辨器主要依靠词汇特征，它们几乎无法推广到未见过的领域。此外，我们还展示了当前的共指消解评估存在明显缺陷，因为只针对一个特定数据集中的特定拆分进行评估，在训练、开发和测试集之间存在显着重叠。