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Incorporating External Knowledge into Machine Reading for Generative Question Answering
[article]
2019
arXiv
pre-print
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
arXiv:1909.02745v1
fatcat:kae4hidiejfm7fbstcv45mz4um