Update README.md

README.md

@@ -1,3 +1,66 @@
 ---
-license: other
+license: lgpl
+multilinguality:
+- monolingual
+language:
+- fr
+task_categories:
+  - other
+task_ids:
+  - word-sense-disambiguation
 ---
+
+# Word Sense Disambiguation for FLUE 
+
+## Dataset Description
+
+- **Homepage:** 
+- **Repository:** 
+- **https://arxiv.org/pdf/1905.05677.pdf** 
+- **Leaderboard:** 
+- **loic.vial@univ-grenoble-alpes.fr** 
+
+### Dataset Summary
+
+This dataset is splitted in 3 sub-datasets: FrenchSemEval-Task12, French WNGT and French SemCor. 
+
+### Supported Tasks and Leaderboards
+
+Word Sense Disambiguation for French.  
+
+### Language
+
+French
+
+
+### Licensing Information
+
+```
+GNU Lesser General Public License
+```
+
+### Citation Information
+
+```bibtex
+@inproceedings{vial-etal-2019-sense,
+    title = "Sense Vocabulary Compression through the Semantic Knowledge of {W}ord{N}et for Neural Word Sense Disambiguation",
+    author = {Vial, Lo{\"\i}c  and
+      Lecouteux, Benjamin  and
+      Schwab, Didier},
+    booktitle = "Proceedings of the 10th Global Wordnet Conference",
+    month = jul,
+    year = "2019",
+    address = "Wroclaw, Poland",
+    publisher = "Global Wordnet Association",
+    url = "https://aclanthology.org/2019.gwc-1.14",
+    pages = "108--117",
+    abstract = "In this article, we tackle the issue of the limited quantity of manually sense annotated corpora for the task of word sense disambiguation, by exploiting the semantic relationships between senses such as synonymy, hypernymy and hyponymy, in order to compress the sense vocabulary of Princeton WordNet, and thus reduce the number of different sense tags that must be observed to disambiguate all words of the lexical database. We propose two different methods that greatly reduce the size of neural WSD models, with the benefit of improving their coverage without additional training data, and without impacting their precision. In addition to our methods, we present a WSD system which relies on pre-trained BERT word vectors in order to achieve results that significantly outperforms the state of the art on all WSD evaluation tasks.",
+}
+
+```
+
+### Contributions
+
+* loic.vial@univ-grenoble-alpes.fr
+* benjamin.lecouteux@univ-grenoble-alpes.fr
+* didier.schwab@univ-grenoble-alpes.fr