论文标题
使用答案选择之间的自然语言关系来理解机器
Using Natural Language Relations between Answer Choices for Machine Comprehension
论文作者
论文摘要
在评估阅读理解任务的答案选择时，其他答案选择可用于该问题，有关同一段落的相关问题的答案通常会提供有价值的信息。在本文中，我们提出了一种方法，以利用答案选择之间的自然语言关系，例如综合和矛盾，以提高机器理解的性能。我们使用独立的问题答案（QA）系统执行QA任务和自然语言推理（NLI）系统来识别选择对之间的关系。然后，我们使用基于整数线性编程（ILP）的关系框架进行推理，以根据NLI系统确定的关系来重新评估独立QA系统做出的决定。我们还提出了一个多任务学习模型，该模型共同学习这两个任务。
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.
