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

Resolving abstract anaphora is an important, but difficult task for text
understanding. Yet, with recent advances in representation learning this task
becomes a more tangible aim. A central property of abstract anaphora is that it
establishes a relation between the anaphor embedded in the anaphoric sentence
and its (typically non-nominal) antecedent. We propose a mention-ranking model
that learns how abstract anaphors relate to their antecedents with an
LSTM-Siamese Net. We overcome the lack of training data by generating
artificial anaphoric sentence--antecedent pairs. Our model outperforms
state-of-the-art results on shell noun resolution. We also report first
benchmark results on an abstract anaphora subset of the ARRAU corpus. This
corpus presents a greater challenge due to a mixture of nominal and pronominal
anaphors and a greater range of confounders. We found model variants that
outperform the baselines for nominal anaphors, without training on individual
anaphor data, but still lag behind for pronominal anaphors. Our model selects
syntactically plausible candidates and -- if disregarding syntax --
discriminates candidates using deeper features.

使用 LSTM-Siamese 网络提出一种提高抽象代词分析性能的注重排名的模型，通过生成人工抽象代词句子 - 先行短语对来克服缺乏训练数据的问题。在 shell 名词分辨率上，我们的模型优于最先进的结果。在包含更多名词和代词的抽象指称子集的 ARRAU 语料库中，我们发现可以优化模型的变体，但在代词指称方面仍然落后。