Abstract
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.
Abstract (translated)
修辞表达（FoS）由多词短语组成，这些短语与文化密切相关。尽管神经机器翻译（NMT）在处理高资源语言中的比喻性表达时表现相对较好，但在处理像僧伽罗语这样的低资源语言时却常常面临挑战，原因在于可利用的数据量有限。为了解决这一局限性，我们构建了一个包含2,344条带有文化和跨语言注释的僧伽罗语修辞表达的语料库。通过对该数据集进行分析，我们将修辞表达的文化起源进行了分类，并确定了它们在不同语言中的等价表达。此外，我们还开发了一种二元分类器来区分数据集中两种类型的FoS，其准确率约为92%。同时，我们也评估了现有大模型在此数据集上的表现。我们的研究发现显示当前大模型的能力存在显著不足，这些模型往往难以准确传达习语的意义。通过将此数据集公开提供，我们为未来在低资源自然语言处理和文化敏感型机器翻译领域的研究提供了重要的基准。
URL
https://arxiv.org/abs/2602.09866
