Automated Knowledge Base Construction (2021) Conference paper One-shot to Weakly-Supervised Relation Classification using Language Models Thy Thy Tran1∗
[email protected] Phong Le2
[email protected] Sophia Ananiadou1;3
[email protected] 1National Centre for Text Mining, The University of Manchester, Manchester, United Kingdom 2Amazon Alexa, Cambridge, UK 3The Alan Turing Institute, London, United Kingdom Abstract Relation classification aims at detecting a particular relation type between two entities in text, whose methods mostly requires annotated data. Data annotation is either a manual process for supervised learning, or automated, using knowledge bases for distant learning. Unfortunately, both annotation methodologies are costly and time-consuming since they depend on intensive human labour for annotation or for knowledge base creation. With recent evidence that language models capture some sort of relational facts as knowledge bases, one-shot relation classification using language models has been proposed via matching a given instance against examples. The only requirement is that each relation type is associated with an exemplar. However, the matching approach often yields incorrect predictions. In this work, we propose NoelA, an auto-encoder using a noisy channel, to improve the accuracy by learning from the matching predictions. NoelA outperforms BERT matching and a bootstrapping baseline on TACRED and reWiki80. 1. Introduction Relation classification (RC)1 detects the connection type between two entities in text such as place of birth between \Murat Kurnaz" and \Germany", Figure1. It is crucial for downstream applications such as knowledge base construction [Ji and Grishman, 2011] and question answering [Xu et al., 2016]. Most work in RC relies on either manually- or automatically-annotated data using knowledge bases (KBs) [Zhang et al., 2017, Mintz et al., 2009, Riedel et al., 2010].