M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis
This provides a resource for researchers in multilingual NLP to benchmark and advance ABSA, though it is incremental as it extends existing datasets to more languages.
The authors tackled the lack of multilingual datasets for aspect-based sentiment analysis by creating M-ABSA, a dataset spanning 7 domains and 21 languages, which they used to evaluate various baselines and demonstrate its utility for tasks like transfer learning and LLM evaluation.
Aspect-based sentiment analysis (ABSA) is a crucial task in information extraction and sentiment analysis, aiming to identify aspects with associated sentiment elements in text. However, existing ABSA datasets are predominantly English-centric, limiting the scope for multilingual evaluation and research. To bridge this gap, we present M-ABSA, a comprehensive dataset spanning 7 domains and 21 languages, making it the most extensive multilingual parallel dataset for ABSA to date. Our primary focus is on triplet extraction, which involves identifying aspect terms, aspect categories, and sentiment polarities. The dataset is constructed through an automatic translation process with human review to ensure quality. We perform extensive experiments using various baselines to assess performance and compatibility on M-ABSA. Our empirical findings highlight that the dataset enables diverse evaluation tasks, such as multilingual and multi-domain transfer learning, and large language model evaluation, underscoring its inclusivity and its potential to drive advancements in multilingual ABSA research.