1 code implementation • 15 Mar 2024 • Shang-Hsuan Chiang, Ssu-Cheng Wang, Yao-Chung Fan
Manually designing cloze test consumes enormous time and efforts.
no code implementations • 2 Dec 2021 • Ying-Hong Chan, Ho-Lam Chung, Yao-Chung Fan
While the significant advancement of QG techniques was reported, current QG results are not ideal for educational reading practice assessment in terms of \textit{controllability} and \textit{question difficulty}.
2 code implementations • 14 Sep 2021 • Wei-Yao Wang, Teng-Fong Chan, Hui-Kuo Yang, Chih-Chuan Wang, Yao-Chung Fan, Wen-Chih Peng
In this paper, we introduce a badminton language to fully describe the process of the shot and propose a deep learning model composed of a novel short-term extractor and a long-term encoder for capturing a shot-by-shot sequence in a badminton rally by framing the problem as predicting a rally result.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Ho-Lam Chung, Ying-Hong Chan, Yao-Chung Fan
In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods.
1 code implementation • 12 Oct 2020 • Ho-Lam Chung, Ying-Hong Chan, Yao-Chung Fan
In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods.
Ranked #1 on Distractor Generation on RACE
1 code implementation • WS 2019 • Ying-Hong Chan, Yao-Chung Fan
In this study, we investigate the employment of the pre-trained BERT language model to tackle question generation tasks.
Ranked #9 on Question Generation on SQuAD1.1 (using extra training data)
no code implementations • WS 2019 • Ying-Hong Chan, Yao-Chung Fan
In this study, we investigate the employment of the pre-trained BERT language model to tackle question generation tasks.