We present RoIt-XMASA, a multilingual dataset that extends the Cross-lingual Multi-domain Amazon Sentiment Analysis to Italian and Romanian, comprising 36,000 labeled reviews across three domains (books, movies, and music) and 202,141 unlabeled samples. To address cross-lingual and cross-domain challenges, we propose a multi-target adversarial training framework that employs loss reversal with meta-learned coefficients to dynamically balance sentiment discrimination with domain and language invariance. XLM-R achieves an F1-score of 66.23% with our approach, outperforming the baseline by 4.64%. Few-shot evaluation shows that Llama-3.1-8B achieves 58.43% F1-score, revealing a meaningful trade-off between the efficiency of prompting-based approaches and the higher performance of task-specific fine-tuning.
@article{arxiv.2604.17134,
title = {RoIt-XMASA: Multi-Domain Multilingual Sentiment Analysis Dataset for Romanian and Italian},
author = {Andrei-Marius Avram and Aureliu Valentin Antonie and Cosmin-Mircea Croitoru and Vlad Andrei Muntean and Dumitru-Clementin Cercel},
journal= {arXiv preprint arXiv:2604.17134},
year = {2026}
}
Comments
Accepted at the International AAAI Conference on Web and Social Media (ICWSM 2026)