Learning to Classify the Wrong Answers for Multiple Choice Question Answering (Student Abstract)
Learning to Classify the Wrong Answers for Multiple Choice Question Answering (Student Abstract)
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DOI：
10.1609/aaai.v34i10.7194
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发表时间：
2020-04
期刊：
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通讯作者：
Hyeon-Jin Kim;Pascale Fung
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文献类型：
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作者：
Hyeon-Jin Kim;Pascale Fung
多项选择题生成（MCQA）是机器阅读理解（MRC）和问题生成（QA）中最具挑战性的领域，因为它不仅需要自然语言理解，还需要问题解决技术。我们提出了一种新的方法，错误答案Entriance（WAE），它可以很容易地应用到各种MCQA任务。为了提高MCQA任务的性能，人们直观地排除不太可能的选项来解决MCQA问题。模仿这种策略，我们用错误答案损失和正确答案损失来训练我们的模型，以概括我们模型的特征，并排除可能但错误的选项。在基于对话的考试数据集上的实验表明了该方法的有效性。我们的方法将微调Transformer的结果提高了2.7%。
Multiple-Choice Question Answering (MCQA) is the most challenging area of Machine Reading Comprehension (MRC) and Question Answering (QA), since it not only requires natural language understanding, but also problem-solving techniques. We propose a novel method, Wrong Answer Ensemble (WAE), which can be applied to various MCQA tasks easily. To improve performance of MCQA tasks, humans intuitively exclude unlikely options to solve the MCQA problem. Mimicking this strategy, we train our model with the wrong answer loss and correct answer loss to generalize the features of our model, and exclude likely but wrong options. An experiment on a dialogue-based examination dataset shows the effectiveness of our approach. Our method improves the results on a fine-tuned transformer by 2.7%.