@inproceedings{wang-etal-2018-semi,
title = "Semi-Supervised Disfluency Detection",
author = "Wang, Feng and
Chen, Wei and
Yang, Zhen and
Dong, Qianqian and
Xu, Shuang and
Xu, Bo",
editor = "Bender, Emily M. and
Derczynski, Leon and
Isabelle, Pierre",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-1299",
pages = "3529--3538",
abstract = "While the disfluency detection has achieved notable success in the past years, it still severely suffers from the data scarcity. To tackle this problem, we propose a novel semi-supervised approach which can utilize large amounts of unlabelled data. In this work, a light-weight neural net is proposed to extract the hidden features based solely on self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN). In addition, we use the unlabelled corpus to enhance the performance. Besides, the Generative Adversarial Network (GAN) training is applied to enforce the similar distribution between the labelled and unlabelled data. The experimental results show that our approach achieves significant improvements over strong baselines.",
}
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<abstract>While the disfluency detection has achieved notable success in the past years, it still severely suffers from the data scarcity. To tackle this problem, we propose a novel semi-supervised approach which can utilize large amounts of unlabelled data. In this work, a light-weight neural net is proposed to extract the hidden features based solely on self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN). In addition, we use the unlabelled corpus to enhance the performance. Besides, the Generative Adversarial Network (GAN) training is applied to enforce the similar distribution between the labelled and unlabelled data. The experimental results show that our approach achieves significant improvements over strong baselines.</abstract>
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%0 Conference Proceedings
%T Semi-Supervised Disfluency Detection
%A Wang, Feng
%A Chen, Wei
%A Yang, Zhen
%A Dong, Qianqian
%A Xu, Shuang
%A Xu, Bo
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F wang-etal-2018-semi
%X While the disfluency detection has achieved notable success in the past years, it still severely suffers from the data scarcity. To tackle this problem, we propose a novel semi-supervised approach which can utilize large amounts of unlabelled data. In this work, a light-weight neural net is proposed to extract the hidden features based solely on self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN). In addition, we use the unlabelled corpus to enhance the performance. Besides, the Generative Adversarial Network (GAN) training is applied to enforce the similar distribution between the labelled and unlabelled data. The experimental results show that our approach achieves significant improvements over strong baselines.
%U https://aclanthology.org/C18-1299
%P 3529-3538
Markdown (Informal)
[Semi-Supervised Disfluency Detection](https://aclanthology.org/C18-1299) (Wang et al., COLING 2018)
ACL
- Feng Wang, Wei Chen, Zhen Yang, Qianqian Dong, Shuang Xu, and Bo Xu. 2018. Semi-Supervised Disfluency Detection. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3529–3538, Santa Fe, New Mexico, USA. Association for Computational Linguistics.