Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering

ICLR 2019 Victor ZhongCaiming XiongNitish Shirish KeskarRichard Socher

End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents... (read more)

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Evaluation results from the paper

Task Dataset Model Metric name Metric value Global rank Compare
Question Answering WikiHop CFC Test 70.6 # 1