Update README.md

README.md

@@ -139,10 +139,26 @@ This category classifies the proverb based on its syntax and how the sentence en
 ## Citation
 
 ```python
-@article{sofalas2026sinfos,
-  title={SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech},
-  author={Sofalas, Johan and Pavithra, Dilushri and Jayatilleke, Nevidu and Weerasinghe, Ruvan},
-  journal={arXiv preprint arXiv:2602.09866},
-  year={2026}
+@inproceedings{sofalas-etal-2026-sinfos,
+    title = "{S}in{F}o{S}: A Parallel Dataset for Translating {S}inhala Figures of Speech",
+    author = "Sofalas, Johan Nevin  and
+      Pavithra, Dilushri  and
+      Jayatilleke, Nevidu  and
+      Weerasinghe, Ruvan",
+    editor = {Ojha, Atul Kr.  and
+      Mititelu, Verginica Barbu  and
+      Constant, Mathieu  and
+      Stoyanova, Ivelina  and
+      Do{\u{g}}ru{\"o}z, A. Seza  and
+      Rademaker, Alexandre},
+    booktitle = "Proceedings of the 22nd Workshop on Multiword Expressions ({MWE} 2026)",
+    month = mar,
+    year = "2026",
+    address = "Rabat, Marocco",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2026.mwe-1.2/",
+    pages = "8--26",
+    ISBN = "979-8-89176-363-0",
+    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."
 }
 ```