通过共性区分选项以实现多项选择题回答
Differentiating Choices via Commonality for Multiple-Choice Question Answering
AI总结：
提出DCQA模型，通过识别并消除选项间的语义共性，利用细微差别作为精炼上下文来区分选项，在五个基准上优于基线模型。
摘要：
当所有选项都与问题相关且语义相似时，多项选择题回答（MCQA）变得尤为具有挑战性。然而，这种MCQA设置可能为选择正确答案提供有价值的线索。现有模型通常分别对每个选项进行排序，忽略了其他选项提供的上下文。具体而言，它们未能利用选项之间的语义共性和细微差别进行推理。在本文中，我们提出了一种新的MCQA模型，通过识别和消除选项的共性来区分选项，称为DCQA。我们的模型捕获每个选项对问题的token级注意力，并将所有选项关注的问题token（即共性）与单个选项关注的问题token（即细微差别）分离开来。使用细微差别作为选项的精炼上下文，我们的模型能够有效区分具有细微差异的选项，并为选择正确答案提供依据。我们在五个常用的MCQA基准上进行了全面实验，证明DCQA始终优于基线模型。此外，我们的案例研究说明了该方法在将模型注意力引导至更具区分性特征方面的有效性。
英文摘要：
Multiple-choice question answering (MCQA) becomes particularly challenging when all choices are relevant to the question and are semantically similar. Yet this setting of MCQA can potentially provide valuable clues for choosing the right answer. Existing models often rank each choice separately, overlooking the context provided by other choices. Specifically, they fail to leverage the semantic commonalities and nuances among the choices for reasoning. In this paper, we propose a novel MCQA model by differentiating choices through identifying and eliminating their commonality, called DCQA. Our model captures token-level attention of each choice to the question, and separates tokens of the question attended to by all the choices (i.e., commonalities) from those by individual choices (i.e., nuances). Using the nuances as refined contexts for the choices, our model can effectively differentiate choices with subtle differences and provide justifications for choosing the correct answer. We conduct comprehensive experiments across five commonly used MCQA benchmarks, demonstrating that DCQA consistently outperforms baseline models. Furthermore, our case study illustrates the effectiveness of the approach in directing the attention of the model to more differentiating features.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
