Search Results for author:
Found 7 papers, 5 papers with code
CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model
Manually designing cloze test consumes enormous time and efforts.
Improving Controllability of Educational Question Generation by Keyword Provision
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}.
Exploring the Long Short-Term Dependencies to Infer Shot Influence in Badminton Matches
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.
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies.
In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods.
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies
In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods.
Ranked #1 on
Distractor Generation
on RACE
A Recurrent BERT-based Model for Question Generation
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)
BERT for Question Generation
In this study, we investigate the employment of the pre-trained BERT language model to tackle question generation tasks.
