Abstract
Sentential relation extraction (RE) is an important task in natural language processing (NLP). In this paper we propose to do sentential RE with dynamic routing in capsules. We first show that the proposed approach outperform state of the art on common sentential relation extraction datasets Tacred, Tacredrev, Retacred, and Conll04. We then investigate potential reasons for its good performance on the mentioned datasets, and yet low performance on another similar, yet larger sentential RE dataset, Wikidata. As such, we identify noise in Wikidata labels as one of the reasons that can hinder performance. Additionally, we show associativity of better performance with better re-representation, a term from neuroscience referred to change of representation in human brain to improve the match at comparison time. As example, in the given analogous terms King:Queen::Man:Woman, at comparison time, and as a result of re-representation, the similarity between related head terms (King,Man), and tail terms (Queen,Woman) increases. As such, our observation show that our proposed model can do re-representation better than the vanilla model compared with. To that end, beside noise in the labels of the distantly supervised RE datasets, we propose re-representation as a challenge in sentential RE.
Abstract (translated)
句子关系抽取（RE）是自然语言处理（NLP）中的一个重要任务。在本文中，我们提出了一种使用胶囊动态路由进行句子级关系抽取的方法。首先，我们展示了所提出的这种方法在常见句子级关系抽取数据集Tacred、Tacredrev、Retacred和Conll04上优于当前最佳性能。然后，我们探讨了它在这类数据集中表现优异的原因，同时在另一个规模更大但相似的句子级RE数据集Wikidata上却表现出较差性能的原因。通过这样的分析，我们识别出Wikidata标签中的噪声可能是影响其性能的一个重要原因。 此外，我们还发现更好的重新表示与更优的表现有相关性，这是神经科学中用来指代人类大脑在比较时改变表示以提高匹配度的概念。“重新表示”一词意指：例如，在给定的类比项King:Queen::Man:Woman（国王：王后 :: 男人：女人）中，通过重新表示，头部术语（King和Man）之间以及尾部术语（Queen和Woman）之间的相似性增加。因此，我们的观察表明，与传统模型相比，我们提出的方法能够更好地进行重新表示。 最后，在除了距离监督RE数据集中标签的噪声之外，我们认为重新表示是句子级关系抽取中的一个挑战。
URL
https://arxiv.org/abs/2508.21049
