论文标题
学会解除关系：与实体引导的关注和混乱感知培训的几乎没有相关的关系分类
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training
论文作者
论文摘要
本文旨在增强少量射击关系分类，特别是对于共同描述多重关系的句子。由于某些关系通常在相同的上下文中保持高相处的事实，因此以前的几个镜头关系分类器很难通过很少的带注释的实例来区分它们。为了减轻上述关系混乱问题，我们提出了CTEG，该模型配备了两种机制，可以学会解除这些易于融合的关系。一方面，引入了一个实体引导的注意（EGA）机制，该机制利用了每个单词和指定实体对之间的句法关系和相对位置，以指导注意力以滤除引起混淆的信息。另一方面，提出了一种混乱感知的培训（CAT）方法，以明确地学习通过在将句子分类为真正的关系及其混乱的关系之间进行推动的游戏来区分关系。在少数数据集上进行了广泛的实验，结果表明，我们提出的模型在准确性方面取得了可比性甚至更好的结果。此外，消融测试和案例研究验证了我们提出的EGA和CAT的有效性，尤其是在解决关系混乱问题时。
This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations. Due to the fact that some relations usually keep high co-occurrence in the same context, previous few-shot relation classifiers struggle to distinguish them with few annotated instances. To alleviate the above relation confusion problem, we propose CTEG, a model equipped with two mechanisms to learn to decouple these easily-confused relations. On the one hand, an Entity-Guided Attention (EGA) mechanism, which leverages the syntactic relations and relative positions between each word and the specified entity pair, is introduced to guide the attention to filter out information causing confusion. On the other hand, a Confusion-Aware Training (CAT) method is proposed to explicitly learn to distinguish relations by playing a pushing-away game between classifying a sentence into a true relation and its confusing relation. Extensive experiments are conducted on the FewRel dataset, and the results show that our proposed model achieves comparable and even much better results to strong baselines in terms of accuracy. Furthermore, the ablation test and case study verify the effectiveness of our proposed EGA and CAT, especially in addressing the relation confusion problem.
