Attention-based Seq2seq Regularisation for Relation Extraction

Relation extraction is an important task for information extraction aiming to detect and extract semantic relationships between entity pairs in sentences. A lot of recently proposed methods for relation extraction have gained remarkable results using deep neural networks. However, a deep network usually requires expensive computational power and is time-consuming when training. To address this problem, we introduce a novel neural relation extraction model that jointly trains a seq2seq regulariser with a classification module. As the regulariser and the classification module share the encoder, the whole model is less complicated compared to novel approaches like BERT. We show that our model achieves the same or better performance than the pre-trained BERT model using sufficient labelled data in much less training time.