MultiAspectEmo: Multilingual and Language-Agnostic Aspect-Based Sentiment Analysis
Joanna Szołomicka, Jan Kocoń · 2022 IEEE International Conference on Data Mining Workshops (ICDMW) · 2022
The paper addresses the important problem of multilingual and language-agnostic approaches to the aspect-based sentiment analysis (ABSA) task, using modern approaches based on transformer models. We propose a new dataset based on automatic translation of the Polish AspectEmo dataset together with cross-lingual transfer of tags describing aspect polarity. The result is a MultiAspectEmo dataset translated into five other languages: English, Czech, Spanish, French and Dutch. In this paper, we also present the original Tr Asp (Transformer-based Aspect Extraction and Classification) method, which is significantly better than methods from the literature in the ABSA task. In addition, we present multilingual and language-agnostic variants of this method, evaluated on the MultiAspectEmo and also the SemEval2016 datasets. We also test various language models for the ABSA task, including compressed models that give promising results while significantly reducing inference time and memory usage.