NollySenti：利用迁移学习与机器翻译实现尼日利亚电影情感分类
NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification
AI总结：
针对非洲语言NLP数据集匮乏、跨领域泛化性差的问题，构建了覆盖尼日利亚五种语言的电影评论情感数据集NollySenti，验证了同领域英语迁移+机器翻译的跨领域情感分类方案效果更优。
摘要：
非洲有超过2000种本土语言，但由于数据集匮乏，这些语言在自然语言处理（NLP）研究中的代表性不足。近年来，非洲语言标注语料库的开发取得了一定进展，但这类语料通常仅覆盖单一领域，难以泛化到其他领域。本文聚焦跨领域适配的情感分类任务，构建了全新数据集NollySenti——该数据集基于诺莱坞（Nollywood）电影评论，覆盖尼日利亚五种广泛使用的语言：英语、豪萨语、伊博语、尼日利亚皮钦语和约鲁巴语。研究使用经典机器学习方法与预训练语言模型开展了全面的实证评估，借助迁移学习对比了来自Twitter领域的跨领域适配性能，以及来自英语的跨语言适配性能。评估结果显示，同目标领域下的英语迁移相比同语言下的Twitter迁移，准确率提升超过5%。为进一步缩小领域差异，研究利用机器翻译（MT）将英语内容译为其他尼日利亚语言，这一方法使性能较跨语言评估进一步提升7%。尽管面向低资源语言的机器翻译质量通常偏低，但人工评估表明，绝大多数翻译句子保留了原始英语评论的情感倾向。
英文摘要：
Africa has over 2000 indigenous languages but they are under-represented in NLP research due to lack of datasets. In recent years, there have been progress in developing labeled corpora for African languages. However, they are often available in a single domain and may not generalize to other domains. In this paper, we focus on the task of sentiment classification for cross domain adaptation. We create a new dataset, NollySenti - based on the Nollywood movie reviews for five languages widely spoken in Nigeria (English, Hausa, Igbo, Nigerian-Pidgin, and Yoruba. We provide an extensive empirical evaluation using classical machine learning methods and pre-trained language models. Leveraging transfer learning, we compare the performance of cross-domain adaptation from Twitter domain, and cross-lingual adaptation from English language. Our evaluation shows that transfer from English in the same target domain leads to more than 5% improvement in accuracy compared to transfer from Twitter in the same language. To further mitigate the domain difference, we leverage machine translation (MT) from English to other Nigerian languages, which leads to a further improvement of 7% over cross-lingual evaluation. While MT to low-resource languages are often of low quality, through human evaluation, we show that most of the translated sentences preserve the sentiment of the original English reviews.
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