Coreferential Reasoning Learningfor Language Representation
Table 1: Performance (F1) on six MRQA extractive question answering benchmarks.
['Model', 'SQuAD', 'NewsQA', 'TriviaQA', 'SearchQA', 'HotpotQA', 'NaturalQA', 'Average']
We find that though the MRQA shared task is not designed for coreferential reasoning, our CorefBERT model still achieves averagely over 1 point improvement on all six datasets, especially on NewsQA and HotpotQA. In NewsQA , 20.7% of the answers can only be inferred by synthesizing information distributed across multiple sentences. In HotpotQA, 63% of the answers need to be inferred through bridge entities or checking multiple properties in different positions. It demonstrates that coreferential reasoning is an essential ability in question answering.