@inproceedings{shetye-etal-2026-star,
    title = "{STAR}-{IL}: A Dataset for Style-Aware Machine Translation of Product Reviews in {I}ndian Languages",
    author = "Shetye, Ketaki  and
      Sharma, Dipti Misra  and
      Krishnamurthy, Parameswari",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-nlpsi/2026.lrec-1.691/",
    doi = "10.63317/4oq85vioi2tu",
    pages = "8780--8793",
    abstract = "Product reviews on e-commerce platforms are a critical form of user-generated content that influence consumer decisions. However, these reviews are predominantly in English, creating a significant accessibility barrier for users who are not fluent in English. When translating into major Indian languages using the current models, the outputs often fail to capture domain-specific features and colloquial style, resulting in stylistically unnatural texts. To address this gap, we introduce \textbf{STAR-IL}, a human-annotated, multilingual, parallel corpus for style-aware translation of product reviews. We evaluate the performance of several state-of-the-art models on our dataset for the task of product review translation. Our experiments show that models fine-tuned on STAR-IL achieve significant average performance gain of \textbf{5.77} points in BLEU and \textbf{3.78} points in COMET, when compared to their baselines, across all languages. Our dataset provides a valuable benchmark for future research in style-aware product review translation. The STAR-IL dataset is publicly available at \url{https://github.com/ltrc/STAR-IL-Corpus}."
}