  End-to-end training has been a popular approach for knowledge base question
answering (KBQA). However, real world applications often contain answers of
varied quality for users' questions. It is not appropriate to treat all
available answers of a user question equally.
  This paper proposes a novel approach based on multiple instance learning to
address the problem of noisy answers by exploring consensus among answers to
the same question in training end-to-end KBQA models. In particular, the QA
pairs are organized into bags with dynamic instance selection and different
options of instance weighting. Curriculum learning is utilized to select
instance bags during training. On the public CQA dataset, the new method
significantly improves both entity accuracy and the Rouge-L score over a
state-of-the-art end-to-end KBQA baseline.
