@inproceedings{kandula-min-2021-improving,
    title = "Improving Cross-Lingual Sentiment Analysis via Conditional Language Adversarial Nets",
    author = "Kandula, Hemanth  and
      Min, Bonan",
    editor = {Vylomova, Ekaterina  and
      Salesky, Elizabeth  and
      Mielke, Sabrina  and
      Lapesa, Gabriella  and
      Kumar, Ritesh  and
      Hammarstr{\"o}m, Harald  and
      Vuli{\'c}, Ivan  and
      Korhonen, Anna  and
      Reichart, Roi  and
      Ponti, Edoardo Maria  and
      Cotterell, Ryan},
    booktitle = "Proceedings of the Third Workshop on Computational Typology and Multilingual NLP",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.sigtyp-1.4/",
    doi = "10.18653/v1/2021.sigtyp-1.4",
    pages = "32--37",
    abstract = "Sentiment analysis has come a long way for high-resource languages due to the availability of large annotated corpora. However, it still suffers from lack of training data for low-resource languages. To tackle this problem, we propose Conditional Language Adversarial Network (CLAN), an end-to-end neural architecture for cross-lingual sentiment analysis without cross-lingual supervision. CLAN differs from prior work in that it allows the adversarial training to be conditioned on both learned features and the sentiment prediction, to increase discriminativity for learned representation in the cross-lingual setting. Experimental results demonstrate that CLAN outperforms previous methods on the multilingual multi-domain Amazon review dataset. Our source code is released at \url{https://github.com/hemanthkandula/clan}."
}