Natural Language Processing tasks such as resolving the coreference of events require understanding the relations between two text snippets. These tasks are typically formulated as (binary) classification problems over independently induced representations of the text snippets. In this work, we develop a Pairwise Representation Learning (PairwiseRL) scheme for the event mention pairs, in which we jointly encode a pair of text snippets so that the representation of each mention in the pair is induced in the context of the other one. Furthermore, our representation supports a finer, structured representation of the text snippet to facilitate encoding events and their arguments. We show that PairwiseRL, despite its simplicity, outperforms the prior state-of-the-art event coreference systems on both cross-document and within-document event coreference benchmarks. We also conduct in-depth analysis in terms of the improvement and the limitation of pairwise representation so as to provide insights for future work.

翻译：自然语言处理任务（如解决事件的共指关系）需要理解两个文本片段之间的关联。这些任务通常被形式化为基于独立推导的文本片段表示的（二值）分类问题。在本文中，我们针对事件提及对提出了一种成对表示学习（PairwiseRL）方案，该方案联合编码一对文本片段，使得每个提及的表示是在另一个提及的上下文中推导得出的。此外，我们的表示支持对文本片段进行更细粒度的结构化表示，以促进事件及其论元的编码。研究表明，尽管PairwiseRL方法简洁，但在跨文档和文档内事件共指基准测试中均优于先前最先进的事件共指系统。我们还从成对表示的改进与局限性两方面进行了深入分析，以期为未来研究提供启示。