@inproceedings{liu-etal-2020-tell,
title = "Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space",
author = "Liu, Dayiheng and
Gong, Yeyun and
Fu, Jie and
Yan, Yu and
Chen, Jiusheng and
Lv, Jiancheng and
Duan, Nan and
Zhou, Ming",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.467",
doi = "10.18653/v1/2020.emnlp-main.467",
pages = "5798--5810",
abstract = "In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks. We treat the question data augmentation task as a constrained question rewriting problem to generate context-relevant, high-quality, and diverse question data samples. CRQDA utilizes a Transformer Autoencoder to map the original discrete question into a continuous embedding space. It then uses a pre-trained MRC model to revise the question representation iteratively with gradient-based optimization. Finally, the revised question representations are mapped back into the discrete space, which serve as additional question data. Comprehensive experiments on SQuAD 2.0, SQuAD 1.1 question generation, and QNLI tasks demonstrate the effectiveness of CRQDA.",
}
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<abstract>In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks. We treat the question data augmentation task as a constrained question rewriting problem to generate context-relevant, high-quality, and diverse question data samples. CRQDA utilizes a Transformer Autoencoder to map the original discrete question into a continuous embedding space. It then uses a pre-trained MRC model to revise the question representation iteratively with gradient-based optimization. Finally, the revised question representations are mapped back into the discrete space, which serve as additional question data. Comprehensive experiments on SQuAD 2.0, SQuAD 1.1 question generation, and QNLI tasks demonstrate the effectiveness of CRQDA.</abstract>
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%0 Conference Proceedings
%T Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
%A Liu, Dayiheng
%A Gong, Yeyun
%A Fu, Jie
%A Yan, Yu
%A Chen, Jiusheng
%A Lv, Jiancheng
%A Duan, Nan
%A Zhou, Ming
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F liu-etal-2020-tell
%X In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks. We treat the question data augmentation task as a constrained question rewriting problem to generate context-relevant, high-quality, and diverse question data samples. CRQDA utilizes a Transformer Autoencoder to map the original discrete question into a continuous embedding space. It then uses a pre-trained MRC model to revise the question representation iteratively with gradient-based optimization. Finally, the revised question representations are mapped back into the discrete space, which serve as additional question data. Comprehensive experiments on SQuAD 2.0, SQuAD 1.1 question generation, and QNLI tasks demonstrate the effectiveness of CRQDA.
%R 10.18653/v1/2020.emnlp-main.467
%U https://aclanthology.org/2020.emnlp-main.467
%U https://doi.org/10.18653/v1/2020.emnlp-main.467
%P 5798-5810
Markdown (Informal)
[Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space](https://aclanthology.org/2020.emnlp-main.467) (Liu et al., EMNLP 2020)
ACL