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End-to-end training has been a popular approach for knowledge base questionanswering (KBQA). However, real world applications often contain answers ofvaried quality for users' questions. It is not appropriate to treat allavailable answers of a user question equally.  This paper proposes a novel approach based on multiple instance learning toaddress the problem of noisy answers by exploring consensus among answers tothe same question in training end-to-end KBQA models. In particular, the QApairs are organized into bags with dynamic instance selection and differentoptions of instance weighting. Curriculum learning is utilized to selectinstance bags during training. On the public CQA dataset, the new methodsignificantly improves both entity accuracy and the Rouge-L score over astate-of-the-art end-to-end KBQA baseline.

Multi-Instance Learning for End-to-End Knowledge Base Question Answering