bitsa_nlp@LT-EDI-ACL2022: Leveraging Pretrained Language Models for Detecting Homophobia and Transphobia in Social Media Comments

• URL: http://arxiv.org/abs/2203.14267v1
• Date: Sun, 27 Mar 2022 10:15:34 GMT
• Title: bitsa_nlp@LT-EDI-ACL2022: Leveraging Pretrained Language Models for Detecting Homophobia and Transphobia in Social Media Comments
• Authors: Vitthal Bhandari and Poonam Goyal
• Abstract summary: We present our system for the LT-EDI shared task on detecting homophobia and transphobia in social media comments. We experiment with a number of monolingual and multilingual transformer based models such as mBERT. We observe their performance on a carefully annotated, real life dataset of YouTube comments in English as well as Tamil.
• Score: 0.9981479937152642
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Online social networks are ubiquitous and user-friendly. Nevertheless, it is vital to detect and moderate offensive content to maintain decency and empathy. However, mining social media texts is a complex task since users don't adhere to any fixed patterns. Comments can be written in any combination of languages and many of them may be low-resource. In this paper, we present our system for the LT-EDI shared task on detecting homophobia and transphobia in social media comments. We experiment with a number of monolingual and multilingual transformer based models such as mBERT along with a data augmentation technique for tackling class imbalance. Such pretrained large models have recently shown tremendous success on a variety of benchmark tasks in natural language processing. We observe their performance on a carefully annotated, real life dataset of YouTube comments in English as well as Tamil. Our submission achieved ranks $9$, $6$ and $3$ with a macro-averaged F1-score of $0.42$, $0.64$ and $0.58$ in the English, Tamil and Tamil-English subtasks respectively. The code for the system has been open sourced.

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