Standard
Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces. / Augenstein, Isabelle; Ruder, Sebastian ; Søgaard, Anders.
Proceedings, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies : (Long Papers). Bind 1 Association for Computational Linguistics, 2018. s. 1896–1906.
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Augenstein, I, Ruder, S
& Søgaard, A 2018,
Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces. i
Proceedings, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies : (Long Papers). bind 1, Association for Computational Linguistics, s. 1896–1906, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, USA,
01/06/2018.
https://doi.org/10.18653/v1/N18-1172
APA
Augenstein, I., Ruder, S.
, & Søgaard, A. (2018).
Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces. I
Proceedings, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies : (Long Papers) (Bind 1, s. 1896–1906). Association for Computational Linguistics.
https://doi.org/10.18653/v1/N18-1172
Vancouver
Augenstein I, Ruder S
, Søgaard A.
Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces. I Proceedings, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies : (Long Papers). Bind 1. Association for Computational Linguistics. 2018. s. 1896–1906
https://doi.org/10.18653/v1/N18-1172
Author
Augenstein, Isabelle ; Ruder, Sebastian ; Søgaard, Anders. / Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces. Proceedings, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies : (Long Papers). Bind 1 Association for Computational Linguistics, 2018. s. 1896–1906
Bibtex
@inproceedings{549949285482479cb402e683b9b39c43,
title = "Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces",
abstract = "We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state of the art for aspect-based and topic-based sentiment analysis.",
author = "Isabelle Augenstein and Sebastian Ruder and Anders S{\o}gaard",
year = "2018",
doi = "10.18653/v1/N18-1172",
language = "English",
volume = "1",
pages = "1896–1906",
booktitle = "Proceedings, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
publisher = "Association for Computational Linguistics",
note = "16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2018 ; Conference date: 01-06-2018 Through 06-06-2018",
}
RIS
TY - GEN
T1 - Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces
AU - Augenstein, Isabelle
AU - Ruder, Sebastian
AU - Søgaard, Anders
PY - 2018
Y1 - 2018
N2 - We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state of the art for aspect-based and topic-based sentiment analysis.
AB - We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state of the art for aspect-based and topic-based sentiment analysis.
U2 - 10.18653/v1/N18-1172
DO - 10.18653/v1/N18-1172
M3 - Article in proceedings
VL - 1
SP - 1896
EP - 1906
BT - Proceedings, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
PB - Association for Computational Linguistics
T2 - 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Y2 - 1 June 2018 through 6 June 2018
ER -
