Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces. Augenstein, I., Ruder, S., & Søgaard, A. 2018. cite arxiv:1802.09913Comment: To appear at NAACL 2018 (long)Paper abstract bibtex 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 sequence classification tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state-of-the-art for aspect- and topic-based sentiment analysis.
@misc{augenstein2018multitask,
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 sequence classification tasks with disparate label spaces. We outperform
strong single and multi-task baselines and achieve a new state-of-the-art for
aspect- and topic-based sentiment analysis.},
added-at = {2018-03-13T08:12:23.000+0100},
author = {Augenstein, Isabelle and Ruder, Sebastian and Søgaard, Anders},
biburl = {https://www.bibsonomy.org/bibtex/210adb9216e6dc322461e858e154b3313/dallmann},
description = {1802.09913.pdf},
interhash = {4fc629ebe1ec8f6c5cfd73975613d8e4},
intrahash = {10adb9216e6dc322461e858e154b3313},
keywords = {deep_learning},
note = {cite arxiv:1802.09913Comment: To appear at NAACL 2018 (long)},
timestamp = {2018-03-13T08:12:23.000+0100},
title = {Multi-task Learning of Pairwise Sequence Classification Tasks Over
Disparate Label Spaces},
url = {http://arxiv.org/abs/1802.09913},
year = 2018
}
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