Papers by Ying-Hong Chan
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies. (2020.findings-emnlp)
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| Challenge: | Existing distractor generation methods are far from practical, and there are still room for improvement. |
| Approach: | They propose a distractor generation scheme with multi-tasking and negative answer training strategies for generating multiple distractors. |
| Outcome: | The proposed scheme improves the state-of-the-art results from 28.65 to 39.81 (BLEU 1 score) and generates multiple distractors shows strong distracting power for multiple choice questions. |
A Recurrent BERT-based Model for Question Generation (D19-58)
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| Challenge: | Existing QG models rely on recurrent neural networks (RNNs) but the inherent sequential nature of the RNN models suffers from the problem of handling long sequences. |
| Approach: | They propose to employ a pre-trained BERT language model to tackle question generation tasks. |
| Outcome: | The proposed model outperforms the existing models on the question-answering dataset SQuAD and advances the BLEU 4 score from 16.85 to 22.17. |