Learning to Classify the Wrong Answers for Multiple Choice Question Answering (Student Abstract)

Authors

  • Hyeondey Kim The Hong Kong University of Science and Technology
  • Pascale Fung The Hong Kong University of Science and Technology

DOI:

https://doi.org/10.1609/aaai.v34i10.7194

Abstract

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%.

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Published

2020-04-03

How to Cite

Kim, H., & Fung, P. (2020). Learning to Classify the Wrong Answers for Multiple Choice Question Answering (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13843-13844. https://doi.org/10.1609/aaai.v34i10.7194

Issue

Section

Student Abstract Track