  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.
