Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification

Xilun Chen, Yu Sun, Ben Athiwaratkun, Claire Cardie, Kilian Weinberger


Abstract
In recent years great success has been achieved in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most languages do not enjoy such an abundance of labeled data. To tackle the sentiment classification problem in low-resource languages without adequate annotated data, we propose an Adversarial Deep Averaging Network (ADAN1) to transfer the knowledge learned from labeled data on a resource-rich source language to low-resource languages where only unlabeled data exist. ADAN has two discriminative branches: a sentiment classifier and an adversarial language discriminator. Both branches take input from a shared feature extractor to learn hidden representations that are simultaneously indicative for the classification task and invariant across languages. Experiments on Chinese and Arabic sentiment classification demonstrate that ADAN significantly outperforms state-of-the-art systems.
Anthology ID:
Q18-1039
Volume:
Transactions of the Association for Computational Linguistics, Volume 6
Month:
Year:
2018
Address:
Venue:
TACL
SIG:
Publisher:
Note:
Pages:
557–570
Language:
URL:
https://www.aclweb.org/anthology/Q18-1039
DOI:
10.1162/tacl_a_00039
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PDF:
http://aclanthology.lst.uni-saarland.de/Q18-1039.pdf
Video:
 https://vimeo.com/306129914