Update README.md

README.md

@@ -21,3 +21,58 @@ This model is licensed under the [CC BY-NC-SA 4.0 License](https://creativecommo
 
 ## Usage
 To use this model for translation, you need to use the prefixes `>>ita<<` for translating to Italian and `>>lld_Latn<<` for translating to Ladin (Val Badia). 
+
+---
+license: cc-by-nc-sa-4.0
+language:
+- it
+- lld
+---
+
+# Ladin-Val Badia to Italian Translation Model
+
+## Description
+This model is designed for translating text between Ladin (Val Badia) and Italian. 
+The model was developed and trained as part of the research presented in the paper titled 
+"Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin" submitted to [LoResMT @ ACL 2024](https://sites.google.com/view/loresmt/). 
+
+## Paper
+The details of the model, including its architecture, training process, and evaluation, are discussed in the paper:
+- [Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin](https://arxiv.org/abs/2407.08819)
+
+## License
+This model is licensed under the [CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+
+## Usage
+To use this model for translation, you need to use the prefixes `>>ita<<` for translating to Italian and `>>lld_Latn<<` for translating to Ladin (Val Badia). 
+
+## Citation
+
+If you use this model, please cite the following paper:
+
+```bibtex
+@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.",
+}
+```
+