In recent years deep neural networks have achieved great success in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most other languages do not enjoy such an abundance of annotated data for sentiment analysis. To tackle this problem, we propose the Adversarial Deep Averaging Network (ADAN) to transfer sentiment knowledge learned from labeled English data to low-resource languages where only unlabeled data exists. ADAN is a "Y-shaped" network with two discriminative branches: a sentiment classifier and an adversarial language identification scorer. Both branches take input from a shared feature extractor that aims to learn hidden representations that capture the underlying sentiment of the text and are invariant across languages. Experiments on Chinese and Arabic sentiment classification demonstrate that ADAN significantly outperforms several baselines, including a strong pipeline approach that relies on state-of-the-art Machine Translation.
['Xilun Chen', 'Yu Sun', 'Ben Athiwaratkun', 'Claire Cardie', 'Kilian Q. Weinberger']
Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification