构建首个斯里兰卡僧伽罗语修辞语料库，助力低资源语言翻译研究
SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech
- 构建2344条僧伽罗语修辞表达数据集，含文化与跨语言标注
- 二分类模型准确率达92%，可区分两类修辞表达类型
- 揭示大模型在习语翻译中的严重缺陷，适合低资源语言研究者
修辞表达是由多词短语构成的、与文化深度关联的语言现象。尽管神经机器翻译在高资源语言中表现良好，但在像僧伽罗语这样的低资源语言上仍面临数据匮乏的挑战。为此，本文构建了一个包含2,344条僧伽罗语修辞表达的并行语料库，并附有文化起源与跨语言对应标注。我们对该数据集进行分析，以识别其文化来源并寻找跨语言等价表达。此外，我们开发了一个二分类器，用于区分数据集中两种类型的修辞表达，准确率约为92%。同时评估了现有大语言模型在此数据集上的表现，结果表明这些模型在传达习语意义时存在明显不足。通过公开该数据集，我们为未来低资源自然语言处理与文化敏感型机器翻译研究提供了重要基准。
原文摘要 · Abstract (English)
Figures of Speech (FoS) consist of multi-word phrases that are deeply intertwined with culture. While Neural Machine Translation (NMT) performs relatively well with the figurative expressions of high-resource languages, it often faces challenges when dealing with low-resource languages like Sinhala due to limited available data. To address this limitation, we introduce a corpus of 2,344 Sinhala figures of speech with cultural and cross-lingual annotations. We examine this dataset to classify the cultural origins of the figures of speech and to identify their cross-lingual equivalents. Additionally, we have developed a binary classifier to differentiate between two types of FOS in the dataset, achieving an accuracy rate of approximately 92%. We also evaluate the performance of existing LLMs on this dataset. Our findings reveal significant shortcomings in the current capabilities of LLMs, as these models often struggle to accurately convey idiomatic meanings. By making this dataset publicly available, we offer a crucial benchmark for future research in low-resource NLP and culturally aware machine translation.
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