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Unsupervised multiple choices question answering via universal corpus

2024-02-27 09:10:28
Qin Zhang, Hao Ge, Xiaojun Chen, Meng Fang


Unsupervised question answering is a promising yet challenging task, which alleviates the burden of building large-scale annotated data in a new domain. It motivates us to study the unsupervised multiple-choice question answering (MCQA) problem. In this paper, we propose a novel framework designed to generate synthetic MCQA data barely based on contexts from the universal domain without relying on any form of manual annotation. Possible answers are extracted and used to produce related questions, then we leverage both named entities (NE) and knowledge graphs to discover plausible distractors to form complete synthetic samples. Experiments on multiple MCQA datasets demonstrate the effectiveness of our method.

Abstract (translated)

无监督问题回答是一个有前景但具有挑战性的任务,这减轻了在新领域建立大规模注释数据集的负担。它激发我们去研究无监督的多选题问题(MCQA)问题。在本文中,我们提出了一个新框架,旨在生成几乎不基于通用领域上下文的新颖MCQA数据,而不依赖于任何形式的手动注释。可能的答案被提取并用于产生相关问题,然后我们利用命名实体(NE)和知识图谱来发现可能的干扰者以形成完整的 synthetic 样本。在多个MCQA数据集上的实验证明了我们的方法的有效性。



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