论文标题
事件核心的成对表示学习
Pairwise Representation Learning for Event Coreference
论文作者
论文摘要
自然语言处理任务，例如解决事件的核心需要了解两个文本片段之间的关系。这些任务通常被表达为（二进制）分类问题，对文本片段的独立表示表示。在这项工作中，我们为事件提及对的成对表示学习（pairwiserl）方案，在其中共同编码一对文本片段，以便在对方的背景下诱导这对中每个提及的表示。此外，我们的表示支持文本片段的精细，结构化表示，以促进编码事件及其论点。我们表明，尽管Pairwiserl具有简单性，但在跨文档和文档内事件核心基准测试上都超过了先前的最新事件核心系统。我们还对成对表示的改进和局限性进行了深入的分析，以便为将来的工作提供见解。
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.
