跨多种非洲语言的情感分析：当前基准
Sentiment Analysis Across Multiple African Languages: A Current Benchmark
AI总结：
本文基于 AfriSenti-SemEval Shared Task 12 数据，对12种非洲语言上的 transformer 模型进行基准测试，发现专为非洲语言开发的模型表现最佳，且更多数据仍能提升单语言模型效果，不存在通用的单一模型解决方案。
摘要：
情感分析是自然语言处理（NLP）中一项基础且重要的任务。然而，由于数据和技术的限制，针对非洲语言的情感分析研究一直较为分散且不足。随着近期 AfriSenti-SemEval Shared Task 12 的发布（作为第17届国际语义评测研讨会的一部分举行），14种非洲语言的标注情感分析数据得以公开。我们在12种语言上对当前最先进的 transformer 模型进行了基准测试和比较，并对比了“每种语言训练一个模型”与“单一模型处理所有语言”的性能。我们还评估了标准多语言模型的性能，以及它们从非非洲语言向非洲语言学习和迁移跨语言表示的能力。结果表明，尽管在低资源建模方面已有研究，但在每种语言的基础上，更多数据仍能产生更好的模型。专门为非洲语言开发的模型在所有任务上均优于其他模型。此外，在针对单一语言的模型评估中，不存在“一模型通吃”的解决方案。而且，对于一些样本量较小的语言，较大的多语言模型在情感分类上可能比专用的单语言模型表现更好。
英文摘要：
Sentiment analysis is a fundamental and valuable task in NLP. However, due to limitations in data and technological availability, research into sentiment analysis of African languages has been fragmented and lacking. With the recent release of the AfriSenti-SemEval Shared Task 12, hosted as a part of The 17th International Workshop on Semantic Evaluation, an annotated sentiment analysis of 14 African languages was made available. We benchmarked and compared current state-of-art transformer models across 12 languages and compared the performance of training one-model-per-language versus single-model-all-languages. We also evaluated the performance of standard multilingual models and their ability to learn and transfer cross-lingual representation from non-African to African languages. Our results show that despite work in low resource modeling, more data still produces better models on a per-language basis. Models explicitly developed for African languages outperform other models on all tasks. Additionally, no one-model-fits-all solution exists for a per-language evaluation of the models evaluated. Moreover, for some languages with a smaller sample size, a larger multilingual model may perform better than a dedicated per-language model for sentiment classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
