Machine reading comprehension methods that generate answers by referring to
multiple passages for a question have gained much attention in AI and NLP
communities. The current methods, however, do not investigate the relationships
among multiple passages in the answer generation process, even though topics
correlated among the passages may be answer candidates. Our method, called
neural answer Generation through Unified Memories over Multiple Passages
(GUM-MP), solves this problem as follows. First, it determines which tokens in
the passages are matched to the question. In particular, it investigates
matches between tokens in positive passages, which are assigned to the
question, and those in negative passages, which are not related to the
question. Next, it determines which tokens in the passage are matched to other
passages assigned to the same question and at the same time it investigates the
topics in which they are matched. Finally, it encodes the token sequences with
the above two matching results into unified memories in the passage encoders
and learns the answer sequence by using an encoder-decoder with a
multiple-pointer-generator mechanism. As a result, GUM-MP can generate answers
by pointing to important tokens present across passages. Evaluations indicate
that GUM-MP generates much more accurate results than the current models do.

该研究提出了一种机器阅读理解方法，使用神经网络编译器在分析与问题相关的正向篇章中的单词以及与问题不相关的反向篇章中的单词的同时，将它们存储在统一记忆中，进而学习并生成答案序列。结果表明，该方法比当前模型产生的结果要精确得多。

多篇文章统一记忆的答案生成

Answer Generation through Unified Memories over Multiple Passages

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。