Update README.md

README.md

@@ -19,6 +19,8 @@ task_categories:
 task_ids:
 - document-retrieval
 paperswithcode_id: bsard
+tags:
+- legal
 ---
 
 # Dataset Card for BSARD
@@ -56,11 +58,11 @@ paperswithcode_id: bsard
 
 ### Dataset Summary
 
-The Belgian Statutory Article Retrieval Dataset (BSARD) v1.0 is a French native corpus for studying statutory article retrieval. BSARD consists of more than 22,600 statutory articles from Belgian law and about 1,100 legal questions posed by Belgian citizens and labeled by experienced jurists with relevant articles from the corpus.
+The Belgian Statutory Article Retrieval Dataset (BSARD) is a French native dataset for studying legal information retrieval. BSARD consists of more than 22,600 statutory articles from Belgian law and about 1,100 legal questions posed by Belgian citizens and labeled by experienced jurists with relevant articles from the corpus.
 
 ### Supported Tasks and Leaderboards
 
-- `document-retrieval`: The dataset can be used to train a model for Ad-Hoc Legal Information Retrieval. An IR model is presented with a short user query written in natural language and asked to retrieve relevant legal information from a knowledge source (such as statutory articles). The model performance is measured by how high its [recall](https://huggingface.co/metrics/recall) score to the reference is. A [BERT-based model](https://huggingface.co/camembert-base) trained as a dense retriever to draw information from statutory articles achieves a recall@100 of 74.8%.
+- `document-retrieval`: The dataset can be used to train models for ad-hoc legal information retrieval. Such model is presented with a short user query written in natural language and asked to retrieve relevant legal information from a knowledge source (such as statutory articles).
 
 ### Languages
 
@@ -177,12 +179,12 @@ BSARD is licensed under the [CC BY-NC-SA 4.0 license](https://creativecommons.or
   year = {2022},
   address = {Dublin, Ireland},
   publisher = {Association for Computational Linguistics},
-  url = {},
-  doi = {},
-  pages = {To appear},
+  url = {https://aclanthology.org/2022.acl-long.468/},
+  doi = {10.18653/v1/2022.acl-long.468},
+  pages = {6789–6803},
 }
 ```
 
 ### Contributions
 
-Thanks to [@antoiloui](https://github.com/antoiloui) for adding this dataset.
+Thanks to [@antoiloui](https://github.com/antoiloui) for adding this dataset.