Yao-Chung Fan


2022

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Keyword Provision Question Generation for Facilitating Educational Reading Comprehension Preparation
Ying-Hong Chan | Ho-Lam Chung | Yao-Chung Fan
Proceedings of the 15th International Conference on Natural Language Generation

2020

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A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies.
Ho-Lam Chung | Ying-Hong Chan | Yao-Chung Fan
Findings of the Association for Computational Linguistics: EMNLP 2020

In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods. First, the quality of the existing DG methods are still far from practical use. There are still room for DG quality improvement. Second, the existing DG designs are mainly for single distractor generation. However, for practical MCQ preparation, multiple distractors are desired. Aiming at these goals, in this paper, we present a new distractor generation scheme with multi-tasking and negative answer training strategies for effectively generating multiple distractors. The experimental results show that (1) our model advances the state-of-the-art result from 28.65 to 39.81 (BLEU 1 score) and (2) the generated multiple distractors are diverse and shows strong distracting power for multiple choice question.

2019

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A Recurrent BERT-based Model for Question Generation
Ying-Hong Chan | Yao-Chung Fan
Proceedings of the 2nd Workshop on Machine Reading for Question Answering

In this study, we investigate the employment of the pre-trained BERT language model to tackle question generation tasks. We introduce three neural architectures built on top of BERT for question generation tasks. The first one is a straightforward BERT employment, which reveals the defects of directly using BERT for text generation. Accordingly, we propose another two models by restructuring our BERT employment into a sequential manner for taking information from previous decoded results. Our models are trained and evaluated on the recent question-answering dataset SQuAD. Experiment results show that our best model yields state-of-the-art performance which advances the BLEU 4 score of the existing best models from 16.85 to 22.17.

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BERT for Question Generation
Ying-Hong Chan | Yao-Chung Fan
Proceedings of the 12th International Conference on Natural Language Generation

In this study, we investigate the employment of the pre-trained BERT language model to tackle question generation tasks. We introduce two neural architectures built on top of BERT for question generation tasks. The first one is a straightforward BERT employment, which reveals the defects of directly using BERT for text generation. And, the second one remedies the first one by restructuring the BERT employment into a sequential manner for taking information from previous decoded results. Our models are trained and evaluated on the question-answering dataset SQuAD. Experiment results show that our best model yields state-of-the-art performance which advances the BLEU4 score of existing best models from 16.85 to 18.91.