We propose an explainable approach for relation extraction that mitigates the tension between generalization and explainability by jointly training for the two goals. Our approach uses a multi-task learning architecture, which jointly trains a classifier for relation extraction, and a sequence model that labels words in the context of the relation that explain the decisions of the relation classifier. We also convert the model outputs to rules to bring global explanations to this approach. This sequence model is trained using a hybrid strategy: supervised, when supervision from pre-existing patterns is available, and semi-supervised otherwise. In the latter situation, we treat the sequence model's labels as latent variables, and learn the best assignment that maximizes the performance of the relation classifier. We evaluate the proposed approach on the two datasets and show that the sequence model provides labels that serve as accurate explanations for the relation classifier's decisions, and, importantly, that the joint training generally improves the performance of the relation classifier. We also evaluate the performance of the generated rules and show that the new rules are great add-on to the manual rules and bring the rule-based system much closer to the neural models.

翻译：我们提出一种可解释的关系提取方法,通过为这两个目标联合培训来缓解一般化和解释之间的矛盾。我们的方法使用一个多任务学习结构,共同培训关系提取的分类师,以及一个序列模型,在解释关系分类师决定的关系中标出文字;我们还将模型输出结果转换成规则,为这一方法提供全球解释。这种序列模型使用混合战略进行培训:监督,在有先前模式监督的情况下,进行半监督。在后一种情况下,我们把序列模型的标签视为潜在的变量,并学习使关系分类师业绩最大化的最佳任务。我们评估了两个数据集的拟议方法,并表明序列模型提供标签,作为关系分类师决定的准确解释,而且重要的是,联合培训通常能改善关系分类员的业绩。我们还评估了所制定规则的绩效,并表明新规则在手工规则中增添了很多内容,使基于规则的系统更接近神经模型。