This paper describes our multiclass classification system developed as part
of the LTEDI@RANLP-2023 shared task. We used a BERT-based language model to
detect homophobic and transphobic content in social media comments across five
language conditions: English, Spanish, Hindi, Malayalam, and Tamil. We
retrained a transformer-based crosslanguage pretrained language model,
XLMRoBERTa, with spatially and temporally relevant social media language data.
We also retrained a subset of models with simulated script-mixed social media
language data with varied performance. We developed the best performing
seven-label classification system for Malayalam based on weighted macro
averaged F1 score (ranked first out of six) with variable performance for other
language and class-label conditions. We found the inclusion of this
spatio-temporal data improved the classification performance for all language
and task conditions when compared with the baseline. The results suggests that
transformer-based language classification systems are sensitive to
register-specific and language-specific retraining.

该研究使用 BERT-based 语言模型开发了多类别分类系统，用于检测社交媒体评论中的恐同和恐 Trans 内容，跨五种语言条件：英语，西班牙语，印地语，马拉雅拉姆语和泰米尔语。发现使用时空相关的社交媒体语言数据可以提高语言分类系统的性能。