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.

This paper presents the system architecture and the technique details adopted by Institute of Scientific and Technical Information of China (ISTIC) in the evaluation of First Conference on EvaHan(2023). In this evaluation, ISTIC participated in two tasks of Ancient Chinese Machine Translation: Ancient Chinese to Modern Chinese and Ancient Chinese to English. The paper mainly elaborates the model framework and data processing methods adopted in ISTIC’s system. Finally a comparison and analysis of different machine translation systems are also given.