Fine-Tuning Encoder-Decoder Models with Contrastive Learning for In-Context Distractor Generation

Distractor generation is the task of automatically generating plausible yet incorrect options (i.e., distractors) for fill-in-the-blank and multiple-choice questions. In assessment, distractors must be contextually relevant to the given question and answer. Even though recent research works focus on fine-tuning pre-trained encoder-decoder models with data augmentation techniques to generate distractors, these models often fail to capture the full semantic representation of a given question-answer and related distractors. The augmentation methods often rely on expanding the quantity of proposed candidates (i.e., questions or distractors), which can introduce noise into the models without necessarily enhancing their understanding of the deeper semantic relationships between question-answer and related distractors. This paper introduces a novel distractor generation model based on contrastive learning to train the model to recognize essential semantic features necessary to generate in-context distractors. The extensive experiments on two public datasets indicate that contrastive learning introduces a strong baseline model to the distractor generation task. It significantly outperforms recent models, increasing the NDCG@3 score from 24.68 to 32.33 on the MCQ dataset and from 26.66 to 36.68 on the SciQ dataset.