@inproceedings{peng-zhang-2020-weighed,
    title = "Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis",
    author = "Peng, Minlong  and
      Zhang, Qi",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.coling-main.22/",
    doi = "10.18653/v1/2020.coling-main.22",
    pages = "251--265",
    abstract = "Cross-domain sentiment analysis is currently a hot topic in both the research and industrial areas. One of the most popular framework for the task is domain-invariant representation learning (DIRL), which aims to learn a distribution-invariant feature representation across domains. However, in this work, we find out that applying DIRL may degrade domain adaptation performance when the label distribution $\textrm{P}(\textrm{Y})$ changes across domains. To address this problem, we propose a modification to DIRL, obtaining a novel weighted domain-invariant representation learning (WDIRL) framework. We show that it is easy to transfer existing models of the DIRL framework to the WDIRL framework. Empirical studies on extensive cross-domain sentiment analysis tasks verified our statements and showed the effectiveness of our proposed solution."
}