Zero-shot Learning for Relation Extraction

Most existing supervised and few-shot learning relation extraction methods have relied on labeled training data. However, in real-world scenarios, there exist many relations for which there is no available training data. We address this issue from the perspective of zero-shot learning (ZSL) which is similar to the way humans learn and recognize new concepts with no prior knowledge. We propose a zero-shot learning relation extraction (ZSLRE) framework, which focuses on recognizing novel relations that have no corresponding labeled data available for training. Our proposed ZSLRE model aims to recognize new relations based on prototypical networks that are modified to utilize side (auxiliary) information. The additional use of side information allows those modified prototype networks to recognize novel relations in addition to recognized previously known relations. We construct side information from labels and their synonyms, hypernyms of name entities, and keywords. We build an automatic hypernym extraction framework to help get hypernyms of various name entities directly from the web. We demonstrate using extensive experiments on two public datasets (NYT and FewRel) that our proposed model significantly outperforms state-of-the-art methods on supervised learning, few-shot learning, and zero-shot learning tasks. Our experimental results also demonstrate the effectiveness and robustness of our proposed model in a combination scenario. Once accepted for publication, we will publish ZSLRE's source code and datasets to enable reproducibility and encourage further research.