DocRED: A Large-Scale Document-Level Relation Extraction Dataset
Table 7: Performance of joint relation and supporting evidence prediction in F1 measurement (%).
['Method', 'Dev', 'Test']
Supporting Evidence Prediction. We propose a new task to predict the supporting evidence for relation instances. On the one hand, jointly predicting the evidence provides better explainability. On the other hand, identifying supporting evidence and reasoning relational facts from text are naturally dual tasks with potential mutual enhancement. We design two supporting evidence prediction methods: (1) Heuristic predictor. We implement a simple heuristic-based model that considers all sentences containing the head or tail entity as supporting evidence. (2) Neural predictor. We also design a neural supporting evidence predictor. Given an entity pair and a predicted relation, sentences are first transformed into input representations by the concatenation of word embeddings and position embeddings, and then fed into a BiLSTM encoder for contextual representations. Inspired by Yang et al.