SinFoS：一种用于翻译僧加罗语修辞表达的并行数据集
SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech
AI总结：
SinFoS数据集通过提供僧加罗语修辞表达的并行文本，评估LLMs在低资源语言和文化敏感翻译中的表现，揭示了现有模型的不足。
摘要：
修辞表达（FoS）由多词短语构成，与文化紧密相关。尽管神经机器翻译（NMT）在高资源语言的修辞表达上表现相对较好，但面对资源较少的语言如僧加罗语时，往往面临数据有限的挑战。为了解决这一限制，我们引入了一个包含2,344个僧加罗语修辞表达的语料库，并附带文化及跨语言注释。我们检查了该数据集以分类修辞表达的文化起源，并识别其跨语言等价物。此外，我们还开发了一个二元分类器，用于区分数据集中的两种类型的FoS，达到了约92%的准确率。我们还评估了现有LLMs在该数据集上的性能。我们的发现揭示了当前LLMs能力的重大不足，因为这些模型往往难以准确传达习语含义。通过公开发布该数据集，我们为未来在低资源NLP和文化敏感机器翻译领域的研究提供了关键的基准。
英文摘要：
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.
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。
