When evaluating an answer choice for Reading Comprehension task, other answer
choices available for the question and the answers of related questions about
the same paragraph often provide valuable information. In this paper, we
propose a method to leverage the natural language relations between the answer
choices, such as entailment and contradiction, to improve the performance of
machine comprehension. We use a stand-alone question answering (QA) system to
perform QA task and a Natural Language Inference (NLI) system to identify the
relations between the choice pairs. Then we perform inference using an Integer
Linear Programming (ILP)-based relational framework to re-evaluate the
decisions made by the standalone QA system in light of the relations identified
by the NLI system. We also propose a multitask learning model that learns both
the tasks jointly.

本文介绍了一种通过利用自然语言关联性来提高机器的阅读理解性能的方法：使用独立的问答系统进行问答任务和自然语言推理系统对选择对之间的关系进行分析，然后通过基于整数线性规划的关系框架进行推理，提高独立问答系统的决策能力。此外，本方法还提出了一个多任务学习模型。