Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction
Recently, question answering (QA) based on machine reading comprehension has become popular. This work focuses on generative QA which aims to generate an abstractive answer to a given question instead of extracting an answer span from a provided passage. Generative QA often suffers from two critical problems: (1) summarizing content irrelevant to a given question, (2) drifting away from a correct answer during generation. In this paper, we address these problems by a novel Rationale-Enriched Answer Generator (REAG), which incorporates an extractive mechanism into a generative model. Specifically, we add an extraction task on the encoder to obtain the rationale for an answer, which is the most relevant piece of text in an input document to a given question. Based on the extracted rationale and original input, the decoder is expected to generate an answer with high confidence. We jointly train REAG on the MS MARCO QA+NLG task and the experimental results show that REAG improves the quality and semantic accuracy of answers over baseline models.
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MS MARCO
