STAR-IL-Corpus: A Dataset for Style-Aware Machine Translation of Product Reviews in Indian Languages

Welcome to the official repository for the STAR-IL: Style-Aware Machine Translation of Product Reviews in Indian Languages dataset.

Overview

STAR-IL is a novel, human-annotated parallel corpus with over 55000 samples covering English-to-Indian Language machine translation, specifically designed to address the challenge of style preservation in translation of product reviews. The dataset captures the colloquial, code-mixed, and domain-specific language inherent in online product reviews. It covers eight languages, namely, Hindi, Marathi, Bengali, Gujarati, Urdu, Kannada, Tamil, and Telugu.

Repository Structure

The dataset is organized into distinct directories corresponding to the source data, translated data, and annotation guidelines:

STAR-IL-Corpus/
├── guidelines/
│   └── guidelines.pdf          # Instructions used by human annotators for translating reviews
├── source_data/
│   ├── benchmark/              # Raw source reviews (TXT) for both fashion and electronics domains
│   └── training/               # Raw source reviews (TXT) for both fashion and electronics domains
└── translated_data/
    ├── benchmark/              # Translated reviews (CSV) across 8 target languages
    └── training/               # Translated reviews (CSV) across 8 target languages

Data Format

All CSV files follow the same column structure:

Column	Description
src	The English product review (Source Text).
tgt	The corresponding human-annotated, style-aware target language translation.
src_lang	Language code for Source (always English).
tgt_lang	Language code for Target (e.g., Hindi, Bengali, Tamil, etc.).
domain	Product category E(electronics) or F(fashion).

Quick Start

You can easily load and explore the dataset using Python and Pandas:

import pandas as pd

# Load the benchmark dataset for a specific language (e.g., Marathi)
df_marathi = pd.read_csv("translated_data/benchmark/Marathi.csv")

# View the first few style-aware translations
print(df_marathi[['src', 'tgt', 'domain']].head())

Citation

If you use the STAR-IL dataset, please cite our paper:

@inproceedings{shetye-etal-2026-star,
  title     = {STAR-IL: A Dataset for Style-Aware Machine Translation of Product Reviews in Indian Languages},
  author    = {Shetye, Ketaki and Sharma, Dipti Misra and Krishnamurthy, Parameswari},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)},
  month     = may,
  year      = {2026},
  pages     = {8780--8793},
  address   = {Palma, Mallorca, Spain},
  publisher = {European Language Resources Association (ELRA)},
  doi       = {10.63317/4oq85vioi2tu},
  abstract  = {Product reviews on e-commerce platforms are a critical form of user-generated content influencing consumer decisions. However, these reviews are predominantly in English, creating accessibility barriers for non-fluent users. Existing translation models often fail to preserve domain-specific features and colloquial style, resulting in unnatural outputs. To address this, we introduce STAR-IL, a human-annotated multilingual parallel corpus for style-aware translation of product reviews. Experiments show that models fine-tuned on STAR-IL achieve an average improvement of 5.77 BLEU and 3.78 COMET points over baselines across all languages. The dataset is publicly available at https://github.com/ltrc/STAR-IL-Corpus.}
}