Transformers changed modern NLP in many ways. However, like many other neural architectures, they are still weak on exploiting domain knowledge and on interpretability. Unfortunately, the exploitation of external, structured knowledge is notoriously prone to a knowledge acquisition bottleneck. We thus propose a memory enhancement of transformer models that makes use of unstructured knowledge. That, expressed in plain text, can be used to carry out classification tasks and as a source of explanations for the model output. An experimental evaluation conducted on two challenging datasets demonstrates that our approach produces relevant explanations without losing in performance.

翻译：变异器在许多方面改变了现代NLP, 但是,与其他许多神经结构一样,它们在利用领域知识和可解释性方面仍然软弱无力。 不幸的是,外部结构化知识的利用极易造成知识获取瓶颈。 因此,我们提议对使用非结构化知识的变异器模型进行记忆强化。 以纯文本表示的,可以用来执行分类任务,并作为模型输出的解释来源。 对两个具有挑战性的数据集进行的实验性评估表明,我们的方法在不亏损性能的情况下产生了相关解释。