@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.",
}
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%0 Conference Proceedings
%T Stance Detection in Code-Mixed Hindi-English Social Media Data using Multi-Task Learning
%A Sane, Sushmitha Reddy
%A Tripathi, Suraj
%A Sane, Koushik Reddy
%A Mamidi, Radhika
%Y Balahur, Alexandra
%Y Klinger, Roman
%Y Hoste, Veronique
%Y Strapparava, Carlo
%Y De Clercq, Orphee
%S Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, USA
%F sane-etal-2019-stance
%X 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.
%R 10.18653/v1/W19-1301
%U https://aclanthology.org/W19-1301
%U https://doi.org/10.18653/v1/W19-1301
%P 1-5
Markdown (Informal)
[Stance Detection in Code-Mixed Hindi-English Social Media Data using Multi-Task Learning](https://aclanthology.org/W19-1301) (Sane et al., WASSA 2019)
ACL