Recent coreference resolution models rely heavily on span representations to
find coreference links between word spans. As the number of spans is $O(n^2)$
in the length of text and the number of potential links is $O(n^4)$, various
pruning techniques are necessary to make this approach computationally
feasible. We propose instead to consider coreference links between individual
words rather than word spans and then reconstruct the word spans. This reduces
the complexity of the coreference model to $O(n^2)$ and allows it to consider
all potential mentions without pruning any of them out. We also demonstrate
that, with these changes, SpanBERT for coreference resolution will be
significantly outperformed by RoBERTa. While being highly efficient, our model
performs competitively with recent coreference resolution systems on the
OntoNotes benchmark.

在对比最近的指代消解模型，并针对文本中的字跨度和潜在联系过多的问题，提出通过单个词汇之间的指代联系，重新构建字跨度来减轻计算量并出色于以前的模型。新模型在 OntoNotes 基准测试中表现出与最近的模型相当的竞争力，且在效率上显著提高。

词级别指代消解

Word-Level Coreference Resolution

We examine the capabilities of a unified, multi-task framework for three
information extraction tasks: named entity recognition, relation extraction,
and event extraction. Our framework (called DyGIE++) accomplishes all tasks by
enumerating, refining, and scoring text spans designed to capture local
(within-sentence) and global (cross-sentence) context. Our framework achieves
state-of-the-art results across all tasks, on four datasets from a variety of
domains. We perform experiments comparing different techniques to construct
span representations. Contextualized embeddings like BERT perform well at
capturing relationships among entities in the same or adjacent sentences, while
dynamic span graph updates model long-range cross-sentence relationships. For
instance, propagating span representations via predicted coreference links can
enable the model to disambiguate challenging entity mentions. Our code is
publicly available at this https URL and can be easily
adapted for new tasks or datasets.

我们提出了一个名为 DyGIE ++ 的统一、多任务框架，并通过枚举、精炼和评分文本跨句的局部和全局环境以完成命名实体识别、关系提取和事件提取等三项信息提取任务，利用动态跨句子关系模拟实现了跨句子实体关系抽取，该框架在多个领域的四个数据集上均取得了最好的结果。