Supervised Relation Classification As Two-way Span-Prediction

Most of the current supervised relation classi-001 fication (RC) algorithms use a single embed-002 ding to represent the relation between a pair 003 of entities. We argue that a better approach 004 is to treat the RC task as a Span-Prediction 005 (SP) problem, similar to Question Answering 006 (QA). We present an SP-based system for RC 007 and evaluate its performance compared to the 008 embedding-based system. We demonstrate 009 that by adding a few improvements, the su-010 pervised SP objective works significantly bet-011 ter than the standard classification-based ob-012 jective. We achieve state-of-the-art results on 013 the TACRED, SemEval task 8, and the CRE 014 datasets. 015