Standard
Sequence classification with human attention. / Barrett, Maria Jung; Bingel, Joachim; Hollenstein, Nora; Rei, Marek; Søgaard, Anders.
Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018). ed. / Anna Korhonen ; Ivan Titov . Association for Computational Linguistics, 2018. p. 302–312.
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Barrett, MJ, Bingel, J, Hollenstein, N, Rei, M & Søgaard, A 2018, Sequence classification with human attention. in A Korhonen & I Titov (eds), Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018). Association for Computational Linguistics, pp. 302–312, 22nd Conference on Computational Natural Language Learning (CoNLL 2018), Brussels, Belgium, 31/10/2018.
APA
Barrett, M. J., Bingel, J., Hollenstein, N., Rei, M., & Søgaard, A. (2018). Sequence classification with human attention. In A. Korhonen , & I. Titov (Eds.), Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018) (pp. 302–312). Association for Computational Linguistics.
Vancouver
Barrett MJ, Bingel J, Hollenstein N, Rei M, Søgaard A. Sequence classification with human attention. In Korhonen A, Titov I, editors, Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018). Association for Computational Linguistics. 2018. p. 302–312
Author
Barrett, Maria Jung ; Bingel, Joachim ; Hollenstein, Nora ; Rei, Marek ; Søgaard, Anders. / Sequence classification with human attention. Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018). editor / Anna Korhonen ; Ivan Titov . Association for Computational Linguistics, 2018. pp. 302–312
Bibtex
@inproceedings{f78fb48ee4a04cec9bf7355e0e958cfb,
title = "Sequence classification with human attention",
abstract = "Learning attention functions requires largevolumes of data, but many NLP tasks simulatehuman behavior, and in this paper, weshow that human attention really does providea good inductive bias on many attentionfunctions in NLP. Specifically, we useestimated human attention derived from eyetrackingcorpora to regularize attention functionsin recurrent neural networks. We showsubstantial improvements across a range oftasks, including sentiment analysis, grammaticalerror detection, and detection of abusivelanguage.",
author = "Barrett, {Maria Jung} and Joachim Bingel and Nora Hollenstein and Marek Rei and Anders S{\o}gaard",
year = "2018",
language = "English",
isbn = "978-1-948087-72-8",
pages = "302–312",
editor = "{Korhonen }, Anna and {Titov }, {Ivan }",
booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018)",
publisher = "Association for Computational Linguistics",
note = "22nd Conference on Computational Natural Language Learning (CoNLL 2018) ; Conference date: 31-10-2018 Through 01-11-2018",
}
RIS
TY - GEN
T1 - Sequence classification with human attention
AU - Barrett, Maria Jung
AU - Bingel, Joachim
AU - Hollenstein, Nora
AU - Rei, Marek
AU - Søgaard, Anders
PY - 2018
Y1 - 2018
N2 - Learning attention functions requires largevolumes of data, but many NLP tasks simulatehuman behavior, and in this paper, weshow that human attention really does providea good inductive bias on many attentionfunctions in NLP. Specifically, we useestimated human attention derived from eyetrackingcorpora to regularize attention functionsin recurrent neural networks. We showsubstantial improvements across a range oftasks, including sentiment analysis, grammaticalerror detection, and detection of abusivelanguage.
AB - Learning attention functions requires largevolumes of data, but many NLP tasks simulatehuman behavior, and in this paper, weshow that human attention really does providea good inductive bias on many attentionfunctions in NLP. Specifically, we useestimated human attention derived from eyetrackingcorpora to regularize attention functionsin recurrent neural networks. We showsubstantial improvements across a range oftasks, including sentiment analysis, grammaticalerror detection, and detection of abusivelanguage.
M3 - Article in proceedings
SN - 978-1-948087-72-8
SP - 302
EP - 312
BT - Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018)
A2 - Korhonen , Anna
A2 - Titov , Ivan
PB - Association for Computational Linguistics
T2 - 22nd Conference on Computational Natural Language Learning (CoNLL 2018)
Y2 - 31 October 2018 through 1 November 2018
ER -
