论文标题
使用证据过滤的上下文建模，用于多项选择问题回答
Context Modeling with Evidence Filter for Multiple Choice Question Answering
论文作者
论文摘要
多项选择性答案（MCQA）是机器阅读理解的一项艰巨的任务。 MCQA的主要挑战是从支持正确答案的给定上下文中提取“证据”。在OpenBookQA数据集中，由于句子在上下文中的相互独立性，提取“证据”的要求尤为重要。现有工作通过带注释的证据或遥远的规则来解决此问题，这些规则过于依赖人类的努力。为了应对挑战，我们提出了一种简单而有效的方法称为证据过滤，以对编码上下文相对于不同选项的关系建模，并可能突出证据句子并过滤掉无关的句子。除了通过在OpenBookQA上进行的大量实验，除了有效地减少人类方法的有效减少，我们表明，所提出的方法的表现优于使用相同的骨干和更多训练数据的模型。我们的参数分析还证明了我们方法的解释性。
Multiple-Choice Question Answering (MCQA) is a challenging task in machine reading comprehension. The main challenge in MCQA is to extract "evidence" from the given context that supports the correct answer. In the OpenbookQA dataset, the requirement of extracting "evidence" is particularly important due to the mutual independence of sentences in the context. Existing work tackles this problem by annotated evidence or distant supervision with rules which overly rely on human efforts. To address the challenge, we propose a simple yet effective approach termed evidence filtering to model the relationships between the encoded contexts with respect to different options collectively and to potentially highlight the evidence sentences and filter out unrelated sentences. In addition to the effective reduction of human efforts of our approach compared, through extensive experiments on OpenbookQA, we show that the proposed approach outperforms the models that use the same backbone and more training data; and our parameter analysis also demonstrates the interpretability of our approach.
