@inproceedings{samanta-etal-2019-improved,
    title = "Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text",
    author = "Samanta, Bidisha  and
      Ganguly, Niloy  and
      Chakrabarti, Soumen",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/P19-1343/",
    doi = "10.18653/v1/P19-1343",
    pages = "3528--3537",
    abstract = "Multilingual writers and speakers often alternate between two languages in a single discourse. This practice is called {\textquotedblleft}code-switching{\textquotedblright}. Existing sentiment detection methods are usually trained on sentiment-labeled monolingual text. Manually labeled code-switched text, especially involving minority languages, is extremely rare. Consequently, the best monolingual methods perform relatively poorly on code-switched text. We present an effective technique for synthesizing labeled code-switched text from labeled monolingual text, which is relatively readily available. The idea is to replace carefully selected subtrees of constituency parses of sentences in the resource-rich language with suitable token spans selected from automatic translations to the resource-poor language. By augmenting the scarce labeled code-switched text with plentiful synthetic labeled code-switched text, we achieve significant improvements in sentiment labeling accuracy (1.5{\%}, 5.11{\%} 7.20{\%}) for three different language pairs (English-Hindi, English-Spanish and English-Bengali). The improvement is even significant in hatespeech detection whereby we achieve a 4{\%} improvement using only synthetic code-switched data (6{\%} with data augmentation)."
}