@inproceedings{sane-etal-2019-stance,
    title = "Stance Detection in Code-Mixed {H}indi-{E}nglish Social Media Data using Multi-Task Learning",
    author = "Sane, Sushmitha Reddy  and
      Tripathi, Suraj  and
      Sane, Koushik Reddy  and
      Mamidi, Radhika",
    editor = "Balahur, Alexandra  and
      Klinger, Roman  and
      Hoste, Veronique  and
      Strapparava, Carlo  and
      De Clercq, Orphee",
    booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-1301",
    doi = "10.18653/v1/W19-1301",
    pages = "1--5",
    abstract = "Social media sites like Facebook, Twitter, and other microblogging forums have emerged as a platform for people to express their opinions and views on different issues and events. It is often observed that people tend to take a stance; in favor, against or neutral towards a particular topic. The task of assessing the stance taken by the individual became significantly important with the emergence in the usage of online social platforms. Automatic stance detection system understands the user{'}s stance by analyzing the standalone texts against a target entity. Due to the limited contextual information a single sentence provides, it is challenging to solve this task effectively. In this paper, we introduce a Multi-Task Learning (MTL) based deep neural network architecture for automatically detecting stance present in the code-mixed corpus. We apply our approach on Hindi-English code-mixed corpus against the target entity - {``}Demonetisation.{''} Our best model achieved the result with a stance prediction accuracy of 63.2{\%} which is a 4.5{\%} overall accuracy improvement compared to the current supervised classification systems developed using the benchmark dataset for code-mixed data stance detection.",
}