  Commonsense and background knowledge is required for a QA model to answer
many nontrivial questions. Different from existing work on knowledge-aware QA,
we focus on a more challenging task of leveraging external knowledge to
generate answers in natural language for a given question with context.
  In this paper, we propose a new neural model, Knowledge-Enriched Answer
Generator (KEAG), which is able to compose a natural answer by exploiting and
aggregating evidence from all four information sources available: question,
passage, vocabulary and knowledge. During the process of answer generation,
KEAG adaptively determines when to utilize symbolic knowledge and which fact
from the knowledge is useful. This allows the model to exploit external
knowledge that is not explicitly stated in the given text, but that is relevant
for generating an answer. The empirical study on public benchmark of answer
generation demonstrates that KEAG improves answer quality over models without
knowledge and existing knowledge-aware models, confirming its effectiveness in
leveraging knowledge.
