A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across different sources to answer correctly. In this paper, we propose two models which make use of multiple passages to generate their answers. Both use an answerreranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model. We propose two methods, namely, strengthbased re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer.
Hence, REF propose strength-base and coverage-based re-ranking approaches, which can aggregate the results extracted from each paragraph by existing DS-QA system to better determine the answer.
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
ArXiv
ArXiv
Computer Science