Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration
arxiv(2024)
Abstract
In this paper, we tackle the task of distractor generation (DG) for
multiple-choice questions. Our study introduces two key designs. First, we
propose retrieval augmented pretraining, which involves refining the
language model pretraining to align it more closely with the downstream task of
DG. Second, we explore the integration of knowledge graphs to enhance the
performance of DG. Through experiments with benchmarking datasets, we show that
our models significantly outperform the state-of-the-art results. Our
best-performing model advances the F1@3 score from 14.80 to 16.47 in MCQ
dataset and from 15.92 to 16.50 in Sciq dataset.
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