Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces

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 semisupervised 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 sequence classification tasks with disparate label spaces. We outperform strong single and multi-Task baselines and achieve a new stateof-the-Art for topic-based sentiment analysis.

AB - We combine multi-Task learning and semisupervised 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 sequence classification tasks with disparate label spaces. We outperform strong single and multi-Task baselines and achieve a new stateof-the-Art for 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 -