Cross-lingual learning enables the transfer of structured sentiment knowledge from high-resource languages to unlabeled or low-resource languages, but prior work has largely focused on coarse-grained sentiment classification or aspect extraction. In contrast, zero-shot cross-lingual aspect–opinion–sentiment triplet extraction (ASTE), which extracts sentiment triplets of the form (aspect term, opinion term, sentiment polarity), remains underexplored. We propose a unified framework that leverages large language models (LLMs) as both structured pseudo-label generators and semantic teachers for ASTE. Our approach employs stepwise structured prompting over aspect- and opinion-aware code-switched variants to generate reliable pseudo triplets, followed by a multi-variant consistency filter to retain high-confidence supervision. We further introduce a triplet-aware contrastive distillation objective that aligns student triplet representations with LLM-encoded semantic embeddings. During inference, only the student ASTE model is used, without requiring LLM access. Experiments on four non-Indic and four low-resource Indic target languages show consistent improvements over strong cross-lingual and LLM-based baselines. The proposed method yields an absolute micro-F1 improvement of 5.3 points on non-Indic languages and 3.8 points on low-resource Indic languages compared to the best competing approach. Ablation results further validate the complementary roles of aspect- and opinion-aware code-switched prompting and triplet-aware contrastive distillation, with larger relative gains observed in low-resource Indic settings.

The rapid growth of online product reviews spurs significant interest in Aspect-Based Sentiment Analysis (ABSA), which involves identifying aspect terms and their associated sentiment polarity. While ABSA is widely studied in resource-rich languages like English, Chinese, and Spanish, it remains underexplored in low-resource languages such as Odia. To address this gap, we create a reliable resource for aspect-based sentiment analysis in Odia. The dataset is annotated for two specific tasks: Aspect Term Extraction (ATE) and Aspect Polarity Classification (APC), spanning seven domains and aligned with the SemEval-2014 benchmark. Furthermore, we employ an ensemble data augmentation approach combining back-translation with a fine-tuned T5 paraphrase generation model to enhance the dataset and apply a semantic similarity filter using a Universal Sentence Encoder (USE) to remove low-quality data and ensure a balanced distribution of sample difficulty in the newly augmented dataset. Finally, we validate our dataset by fine-tuning multilingual pre-trained models, XLM-R and IndicBERT, on ATE and APC tasks. Additionally, we use three classical baseline models to evaluate the quality of the proposed dataset for these tasks. We hope the Odia dataset will spur more work for the ABSA task.