LASQ: A Low-resource Aspect-based Sentiment Quadruple Extraction Dataset
This work provides a benchmark and method for fine-grained sentiment extraction in low-resource agglutinative languages, addressing a gap in ABSA research.
The authors constructed the first low-resource aspect-based sentiment quadruple dataset for Uzbek and Uyghur, and proposed a grid-tagging model with syntactic knowledge that achieved consistent gains over baselines.
In recent years, aspect-based sentiment analysis (ABSA) has made rapid progress and shown strong practical value. However, existing research and benchmarks are largely concentrated on high-resource languages, leaving fine-grained sentiment extraction in low-resource languages under-explored. To address this gap, we constructed the first Low-resource languages Aspect-based Sentiment Quadruple dataset, named LASQ, which includes two low-resource languages: Uzbek and Uyghur. Secondly, it includes a fine-grained target-aspect-opinion-sentiment quadruple extraction task. To facilitate future research, we designed a grid-tagging model that integrates syntactic knowledge. This model incorporates part-of-speech (POS) and dependency knowledge into the model through our designed Syntax Knowledge Embedding Module (SKEM), thereby alleviating the lexical sparsity problem caused by agglutinative languages. Experiments on LASQ demonstrate consistent gains over competitive baselines, validating both the dataset's utility and the effectiveness of the proposed modeling approach.