@inproceedings{alex-eponon-etal-2024-pinealai,
    title = "{P}inealai{\_}{S}tress{I}dent{\_}{LT}-{EDI}@{EACL}2024: Minimal configurations for Stress Identification in {T}amil and {T}elugu",
    author = "Alex Eponon, Anvi  and
      Batyrshin, Ildar  and
      Sidorov, Grigori",
    editor = {Chakravarthi, Bharathi Raja  and
      B, Bharathi  and
      Buitelaar, Paul  and
      Durairaj, Thenmozhi  and
      Kov{\'a}cs, Gy{\"o}rgy  and
      Garc{\'i}a Cumbreras, Miguel {\'A}ngel},
    booktitle = "Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = mar,
    year = "2024",
    address = "St. Julian's, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.ltedi-1.15/",
    pages = "152--156",
    abstract = "This paper introduces an approach to stress identification in Tamil and Telugu, leveraging traditional machine learning models{---}Fasttext for Tamil and Naive Bayes for Telugu{---}yielding commendable results. The study highlights the scarcity of annotated data and recognizes limitations in phonetic features relevant to these languages, impacting precise information extraction. Our models achieved a macro F1 score of 0.77 for Tamil and 0.72 for Telugu with Fasttext and Naive Bayes, respectively. While the Telugu model secured the second rank in shared tasks, ongoing research is crucial to unlocking the full potential of stress identification in these languages, necessitating the exploration of additional features and advanced techniques specified in the discussions and limitations section."
}