@inproceedings{jamshid-lou-johnson-2020-improving,
title = "Improving Disfluency Detection by Self-Training a Self-Attentive Model",
author = "Jamshid Lou, Paria and
Johnson, Mark",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.346",
doi = "10.18653/v1/2020.acl-main.346",
pages = "3754--3763",
abstract = "Self-attentive neural syntactic parsers using contextualized word embeddings (e.g. ELMo or BERT) currently produce state-of-the-art results in joint parsing and disfluency detection in speech transcripts. Since the contextualized word embeddings are pre-trained on a large amount of unlabeled data, using additional unlabeled data to train a neural model might seem redundant. However, we show that self-training {---} a semi-supervised technique for incorporating unlabeled data {---} sets a new state-of-the-art for the self-attentive parser on disfluency detection, demonstrating that self-training provides benefits orthogonal to the pre-trained contextualized word representations. We also show that ensembling self-trained parsers provides further gains for disfluency detection.",
}
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%0 Conference Proceedings
%T Improving Disfluency Detection by Self-Training a Self-Attentive Model
%A Jamshid Lou, Paria
%A Johnson, Mark
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F jamshid-lou-johnson-2020-improving
%X Self-attentive neural syntactic parsers using contextualized word embeddings (e.g. ELMo or BERT) currently produce state-of-the-art results in joint parsing and disfluency detection in speech transcripts. Since the contextualized word embeddings are pre-trained on a large amount of unlabeled data, using additional unlabeled data to train a neural model might seem redundant. However, we show that self-training — a semi-supervised technique for incorporating unlabeled data — sets a new state-of-the-art for the self-attentive parser on disfluency detection, demonstrating that self-training provides benefits orthogonal to the pre-trained contextualized word representations. We also show that ensembling self-trained parsers provides further gains for disfluency detection.
%R 10.18653/v1/2020.acl-main.346
%U https://aclanthology.org/2020.acl-main.346
%U https://doi.org/10.18653/v1/2020.acl-main.346
%P 3754-3763
Markdown (Informal)
[Improving Disfluency Detection by Self-Training a Self-Attentive Model](https://aclanthology.org/2020.acl-main.346) (Jamshid Lou & Johnson, ACL 2020)
ACL