论文标题
跨文献的对比表示学习事件和实体的核心核心分辨率
Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities
论文作者
论文摘要
识别文档内部和跨文档中的相关实体和事件是自然语言理解的基础。我们提出了一种利用对比表示学习的实体和事件核心解决方案的方法。较早的最新方法已将此问题作为二进制分类问题提出，并利用跨编码器体系结构中的大型变压器来实现其结果。对于大量文档和相应的$ n $提及集，在这些早期方法中执行$ n^{2} $计算的必要性可以是计算密集的。我们表明，可以通过应用仅需要在推理时间进行$ n $变压器计算的对比学习技术来减轻这种负担。我们的方法可以在欧洲央行+语料库上的许多关键指标上实现最新的结果，并且在其他欧洲央行上具有竞争力。
Identifying related entities and events within and across documents is fundamental to natural language understanding. We present an approach to entity and event coreference resolution utilizing contrastive representation learning. Earlier state-of-the-art methods have formulated this problem as a binary classification problem and leveraged large transformers in a cross-encoder architecture to achieve their results. For large collections of documents and corresponding set of $n$ mentions, the necessity of performing $n^{2}$ transformer computations in these earlier approaches can be computationally intensive. We show that it is possible to reduce this burden by applying contrastive learning techniques that only require $n$ transformer computations at inference time. Our method achieves state-of-the-art results on a number of key metrics on the ECB+ corpus and is competitive on others.
