Update README.md

README.md

@@ -14,18 +14,44 @@ base_model:
 
 # Llamipa: An Incremental Discourse Parser
 
-Llamipa is an LLM (Llama3-8B) finetuned on the Minecraft Structured Dialogue Corpus (MSDC) https://github.com/linagora-labs/MinecraftStucturedDialogueCorpus. 
+Llamipa is an LLM (Llama3-8B) finetuned on the Minecraft Structured Dialogue Corpus (MSDC) https://huggingface.co/datasets/linagora/MinecraftStructuredDialogueCorpus.
 
-## Model Details
-We provide the adapters for the Llamipa parser trained on Llama3-8B, the generation script, as well as the formatted MSDC test data. 
+We provide the adapters for the Llamipa parser trained on Llama3-8B, the generation script, as well as the formatted MSDC data. We also provide scripts for formatting new dialogue data you may wish to try with Llamipa. 
 
 ### Model Description
 
 - **Language(s) (NLP):** English
 - **Finetuned from model:** Llama3-8B
 
-### Model Sources
+### Citations
 
 **Paper:** https://aclanthology.org/2024.findings-emnlp.373/
+**Video:** https://www.youtube.com/watch?v=yerUotx3QZY 
+
+Please cite the EMNLP Findings paper if you use Llamipa in your work
+
+```bibtex
+@inproceedings{thompson-etal-2024-llamipa,
+    title = "Llamipa: An Incremental Discourse Parser",
+    author = "Thompson, Kate  and
+      Chaturvedi, Akshay  and
+      Hunter, Julie  and
+      Asher, Nicholas",
+    editor = "Al-Onaizan, Yaser  and
+      Bansal, Mohit  and
+      Chen, Yun-Nung",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
+    month = nov,
+    year = "2024",
+    address = "Miami, Florida, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.findings-emnlp.373/",
+    doi = "10.18653/v1/2024.findings-emnlp.373",
+    pages = "6418--6430"
+}
+```
+
+### Acknowledgements 
+
+We acknowledge support from the National Interdisciplinary Artificial Intelligence Institute, ANITI (Artificial and Natural Intelligence Toulouse Institute), funded by the French ‘Investing for the Future–PIA3’ program under the Grant agreement ANR-19-PI3A-000. We also thank the ANR project COCOBOTS (ANR-21-FAI2-0005), the ANR/DGA project DISCUTER (ANR21-ASIA-0005), and the COCOPIL “Graine” project funded by the Région Occitanie of France. This work was granted access to the HPC resources of CALMIP supercomputing center under the allocation 2016-P23060.
 
-**Video:** https://www.youtube.com/watch?v=yerUotx3QZY