Multi-choice Machine Reading Comprehension (MRC) requires model to decide the
correct answer from a set of answer options when given a passage and a
question. Thus in addition to a powerful Pre-trained Language Model (PrLM) as
encoder, multi-choice MRC especially relies on a matching network design which
is supposed to effectively capture the relationships among the triplet of
passage, question and answers. While the newer and more powerful PrLMs have
shown their mightiness even without the support from a matching network, we
propose a new DUal Multi-head Co-Attention (DUMA) model, which is inspired by
human's transposition thinking process solving the multi-choice MRC problem:
respectively considering each other's focus from the standpoint of passage and
question. The proposed DUMA has been shown effective and is capable of
generally promoting PrLMs. Our proposed method is evaluated on two benchmark
multi-choice MRC tasks, DREAM and RACE, showing that in terms of powerful
PrLMs, DUMA can still boost the model to reach new state-of-the-art
performance.

本文提出一种新的多选机器阅读理解模型 (DUMA)，基于人类转位思维解决多选机器阅读理解问题，通过分别考虑语段和问题的焦点来实现匹配，提高了模型的性能，在 DREAM 和 RACE 两个基准多选 MRC 任务中显示其强大状态 - of-the-art 的性能。