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 answer-reranking approach which reorders the answer candidates generated
by an existing state-of-the-art QA model. We propose two methods, namely,
strength-based re-ranking and coverage-based re-ranking, to make use of the
aggregated evidence from different passages to better determine the answer. Our
models have achieved state-of-the-art results on three public open-domain QA
datasets: Quasar-T, SearchQA and the open-domain version of TriviaQA, with
about 8 percentage points of improvement over the former two datasets.

本文提出了两种新的模型，在公开领域问答数据集中取得了最具竞争力的结果。这两种方法是使用多篇文章生成答案。