@inproceedings{chung-etal-2020-bert,
title = "A {BERT}-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies.",
author = "Chung, Ho-Lam and
Chan, Ying-Hong and
Fan, Yao-Chung",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.393",
doi = "10.18653/v1/2020.findings-emnlp.393",
pages = "4390--4400",
abstract = "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 \textit{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.",
}
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%0 Conference Proceedings
%T A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies.
%A Chung, Ho-Lam
%A Chan, Ying-Hong
%A Fan, Yao-Chung
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F chung-etal-2020-bert
%X 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.
%R 10.18653/v1/2020.findings-emnlp.393
%U https://aclanthology.org/2020.findings-emnlp.393
%U https://doi.org/10.18653/v1/2020.findings-emnlp.393
%P 4390-4400
Markdown (Informal)
[A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies.](https://aclanthology.org/2020.findings-emnlp.393) (Chung et al., Findings 2020)
ACL