# SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech

arXiv: https://arxiv.org/abs/2602.09866 · HTML: https://mlsanity.com/paper/2602.09866/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 92 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 24 of 24)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [ChID: A Large-scale Chinese IDiom Dataset for Cloze Test](https://mlsanity.com/paper/1906.01265/) (2019)
- [Understanding Transformer Memorization Recall Through Idioms](https://mlsanity.com/paper/2210.03588/) (2022)
- [Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World](https://mlsanity.com/paper/2210.08523/) (2022)
- [MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding](https://mlsanity.com/paper/2103.06779/) (2021)
- [Multi-lingual and Multi-cultural Figurative Language Understanding](https://mlsanity.com/paper/2305.16171/) (2023)
- [Survey on Publicly Available Sinhala Natural Language Processing Tools and Research](https://mlsanity.com/paper/1906.02358/) (2019)
- [SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding](https://mlsanity.com/paper/2204.10050/) (2022)
- [Examining the Tip of the Iceberg: A Data Set for Idiom Translation](https://mlsanity.com/paper/1802.04681/) (2018)
- [AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models](https://mlsanity.com/paper/2109.04413/) (2021)
- [Figurative Language in Recognizing Textual Entailment](https://mlsanity.com/paper/2106.01195/) (2021)
- [EPIE Dataset: A Corpus For Possible Idiomatic Expressions](https://mlsanity.com/paper/2006.09479/) (2020)
- [Translate Meanings, Not Just Words: IdiomKB's Role in Optimizing Idiomatic Translation with Language Models](https://mlsanity.com/paper/2308.13961/) (2023)
- [Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translation](https://mlsanity.com/paper/1704.06836/) (2017)
- [Improving LLM Abilities in Idiomatic Translation](https://mlsanity.com/paper/2407.03518/) (2024)
- [LIDIOMS: A Multilingual Linked Idioms Data Set](https://mlsanity.com/paper/1802.08148/) (2018)
- [Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms](https://mlsanity.com/paper/2105.03280/) (2021)
- [ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding](https://mlsanity.com/paper/2109.06838/) (2021)
- [PETCI: A Parallel English Translation Dataset of Chinese Idioms](https://mlsanity.com/paper/2202.09509/) (2022)
- [Zero-shot OCR Accuracy of Low-Resourced Languages: A Comparative Analysis on Sinhala and Tamil](https://mlsanity.com/paper/2507.18264/) (2025)
- [CoAM: Corpus of All-Type Multiword Expressions](https://mlsanity.com/paper/2412.18151/) (2024)
- [Neural embeddings for metaphor detection in a corpus of Greek texts](https://mlsanity.com/paper/1902.03659/) (2019)
- [Beyond Vanilla Fine-Tuning: Leveraging Multistage, Multilingual, and Domain-Specific Methods for Low-Resource Machine Translation](https://mlsanity.com/paper/2503.22582/) (2025)

## Built on by (most cited first, 1 of 1)

- [SiPaKosa: A Comprehensive Corpus of Canonical and Classical Buddhist Texts in Sinhala and Pali](https://mlsanity.com/paper/2603.29221/) (2026)

## Abstract (arXiv)

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.