构建首个覆盖21语言7领域的多语言情感分析数据集
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis
- 自动翻译+人工校验构建跨语言数据
- 支持跨语言、跨领域迁移学习任务
- 适合多语言NLP与情感分析研究者
基于方面的情感分析（ABSA）是信息提取与情感分析中的关键任务，旨在识别文本中的方面项及其情感极性。然而现有ABSA数据集以英语为主，限制了多语言研究与评估。为此，我们提出M-ABSA，一个涵盖7个领域、21种语言的综合性多语言平行数据集，是目前最广泛的多语言ABSA数据集。研究聚焦于三元组抽取，即识别方面词、方面类别和情感极性。数据通过自动化翻译结合人工审核构建，确保质量。我们在多种基线模型上进行广泛实验，验证其在多语言和多领域迁移学习、大模型评估等方面的表现。实证结果表明，该数据集可支持多样化的评估任务，凸显其包容性与推动多语言ABSA研究的潜力。
原文摘要 · Abstract (English)
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.
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