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.

翻译：在多选题问答任务中，当所有选项均与问题相关且语义相似时，答案选择变得尤为困难。然而，这种多选题设置可能为选择正确答案提供有价值的线索。现有模型通常单独对每个选项进行排序，忽略了其他选项提供的上下文信息。具体而言，这些模型未能利用选项间的语义共性与细微差异进行推理。本文提出一种新颖的多选题问答模型DCQA，该模型通过识别并消除选项间的共性来实现选项区分。我们的模型捕捉每个选项对问题的词元级注意力，并将被所有选项共同关注的问句词元（即共性）与仅被单个选项关注的词元（即细微差异）进行分离。利用这些细微差异作为选项的精细化上下文，我们的模型能够有效区分具有微妙差异的选项，并为选择正确答案提供依据。我们在五个常用多选题问答基准数据集上进行了全面实验，结果表明DCQA模型始终优于基线模型。此外，案例研究表明该方法能有效引导模型关注更具区分性的特征。