Update README.md

README.md

@@ -36,9 +36,27 @@ Testset 1 consists of parallel sentences in Ladin and Italian. The dataset conta
 If you use this dataset, please cite the following paper:
 
 ```bibtex
-@article{Frontull:Moser:2024,
-  title={Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin},
-  author={Frontull, Samuel and Moser, Georg},
-  journal={arXiv preprint arXiv:2407.08819},
-  year={2024}
+@inproceedings{frontull-moser-2024-rule,
+    title = "Rule-Based, Neural and {LLM} Back-Translation: Comparative Insights from a Variant of {L}adin",
+    author = "Frontull, Samuel  and
+      Moser, Georg",
+    editor = "Ojha, Atul Kr.  and
+      Liu, Chao-hong  and
+      Vylomova, Ekaterina  and
+      Pirinen, Flammie  and
+      Abbott, Jade  and
+      Washington, Jonathan  and
+      Oco, Nathaniel  and
+      Malykh, Valentin  and
+      Logacheva, Varvara  and
+      Zhao, Xiaobing",
+    booktitle = "Proceedings of the The Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)",
+    month = aug,
+    year = "2024",
+    address = "Bangkok, Thailand",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.loresmt-1.13",
+    pages = "128--138",
+    abstract = "This paper explores the impact of different back-translation approaches on machine translation for Ladin, specifically the Val Badia variant. Given the limited amount of parallel data available for this language (only 18k Ladin-Italian sentence pairs), we investigate the performance of a multilingual neural machine translation model fine-tuned for Ladin-Italian. In addition to the available authentic data, we synthesise further translations by using three different models: a fine-tuned neural model, a rule-based system developed specifically for this language pair, and a large language model. Our experiments show that all approaches achieve comparable translation quality in this low-resource scenario, yet round-trip translations highlight differences in model performance.",
 }
+```