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.

翻译：我们提出RoIt-XMASA，一个将跨语言多领域亚马逊情感分析扩展至意大利语和罗马尼亚语的多语言数据集，包含36,000条标注评论（覆盖图书、电影、音乐三个领域）及202,141条无标注样本。为应对跨语言与跨领域挑战，我们提出一种多目标对抗训练框架，通过元学习系数实现损失反转，动态平衡情感判别与领域/语言不变性。采用该方法的XLM-R模型F1分数达66.23%，相较于基线提升4.64%。少样本评估显示，Llama-3.1-8B取得58.43%的F1分数，揭示了基于提示的方法的效率与任务特定微调的高性能之间存在有意义的权衡。