@inproceedings{jain-etal-2018-learning,
title = "Learning Disentangled Representations of Texts with Application to Biomedical Abstracts",
author = "Jain, Sarthak and
Banner, Edward and
van de Meent, Jan-Willem and
Marshall, Iain J. and
Wallace, Byron C.",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1497",
doi = "10.18653/v1/D18-1497",
pages = "4683--4693",
abstract = "We propose a method for learning disentangled representations of texts that code for distinct and complementary aspects, with the aim of affording efficient model transfer and interpretability. To induce disentangled embeddings, we propose an adversarial objective based on the (dis)similarity between triplets of documents with respect to specific aspects. Our motivating application is embedding biomedical abstracts describing clinical trials in a manner that disentangles the populations, interventions, and outcomes in a given trial. We show that our method learns representations that encode these clinically salient aspects, and that these can be effectively used to perform aspect-specific retrieval. We demonstrate that the approach generalizes beyond our motivating application in experiments on two multi-aspect review corpora.",
}
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%0 Conference Proceedings
%T Learning Disentangled Representations of Texts with Application to Biomedical Abstracts
%A Jain, Sarthak
%A Banner, Edward
%A van de Meent, Jan-Willem
%A Marshall, Iain J.
%A Wallace, Byron C.
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F jain-etal-2018-learning
%X We propose a method for learning disentangled representations of texts that code for distinct and complementary aspects, with the aim of affording efficient model transfer and interpretability. To induce disentangled embeddings, we propose an adversarial objective based on the (dis)similarity between triplets of documents with respect to specific aspects. Our motivating application is embedding biomedical abstracts describing clinical trials in a manner that disentangles the populations, interventions, and outcomes in a given trial. We show that our method learns representations that encode these clinically salient aspects, and that these can be effectively used to perform aspect-specific retrieval. We demonstrate that the approach generalizes beyond our motivating application in experiments on two multi-aspect review corpora.
%R 10.18653/v1/D18-1497
%U https://aclanthology.org/D18-1497
%U https://doi.org/10.18653/v1/D18-1497
%P 4683-4693
Markdown (Informal)
[Learning Disentangled Representations of Texts with Application to Biomedical Abstracts](https://aclanthology.org/D18-1497) (Jain et al., EMNLP 2018)
ACL