Learning from Context or Names? An Empirical Study on Neural Relation Extraction

Understand

Neural models have achieved remarkable success on relation extraction (RE) benchmarks.

• However, there is no clear understanding which type of information affects existing RE models to make decisions and how to further improve the performance of these models.
• To this end, we empirically study the effect of two main information sources in text: textual context and entity mentions (names).
• We find that (i) while context is the main source to support the predictions, RE models also heavily rely on the information from entity mentions, most of which is type information, and (ii) existing datasets may leak shallow heuristics via entity mentions and thus contribute to the high performance on RE benchmarks.