Update README.md

README.md

@@ -3,7 +3,6 @@ annotations_creators:
 - expert-generated
 language_creators:
 - found
-- other
 languages:
 - fr-BE
 licenses:
@@ -51,8 +50,8 @@ task_ids:
 ## Dataset Description
 
 - **Repository:** [https://github.com/maastrichtlawtech/bsard](https://github.com/maastrichtlawtech/bsard)
-- **Paper:** [https://arxiv.org/abs/2108.11792](https://arxiv.org/abs/2108.11792)
-- **Point of Contact:** [law-techlab@maastrichtuniversity.nl](law-techlab@maastrichtuniversity.nl)
+- **Paper:** [A Statutory Article Retrieval Dataset in French](https://arxiv.org/abs/2108.11792)
+- **Point of Contact:** [Maastricht Law & Tech Lab](law-techlab@maastrichtuniversity.nl)
 
 ### Dataset Summary
 
@@ -74,31 +73,33 @@ A typical data point comprises a question, with additional `category`, `subcateg
 
 An example from the BSARD test set looks as follows:
 ```
-{'id': '724',
+{
+ 'id': '724',
  'question': 'La police peut-elle me fouiller pour chercher du cannabis ?',
  'category': 'Justice',
  'subcategory': 'Petite délinquance',
  'extra_description': 'Détenir, acheter et vendre du cannabis',
- 'article_ids': '13348'}
+ 'article_ids': '13348'
+}
 ```
 
 ### Data Fields
 
-- In *questions_fr_train.csv* and *questions_fr_test.csv*:
-  - `id`: a `int32` feature corresponding to a unique ID number for the question.
-  - `question`: a `string` feature corresponding to the question.
-  - `category`: a `string` feature corresponding to the general topic of the question.
-  - `subcategory`: a `string` feature corresponding to the sub-topic of the question.
-  - `extra_description`: a `string` feature corresponding to the extra categorization tags of the question.
-  - `article_ids`: a `string` feature of comma-separated article IDs relevant to the question.
-
-- In *articles_fr.csv*:
-  - `id`: a `int32` feature corresponding to a unique ID number for the article.
-  - `article`: a `string` feature corresponding to the full article.
-  - `code`: a `string` feature corresponding to the law code to which the article belongs.
-  - `article_no`: a `string` feature corresponding to the article number in the code.
-  - `description`: a `string` feature corresponding to the concatenated headings of the article.
-  - `law_type`: a `string` feature whose value is either *"regional"* or *"national"*.
+- In **"questions_fr_train.csv"** and **"questions_fr_test.csv"**:
+  - `id`: an *int32* feature corresponding to a unique ID number for the question.
+  - `question`: a *string* feature corresponding to the question.
+  - `category`: a *string* feature corresponding to the general topic of the question.
+  - `subcategory`: a *string* feature corresponding to the sub-topic of the question.
+  - `extra_description`: a *string* feature corresponding to the extra categorization tags of the question.
+  - `article_ids`: a *string* feature of comma-separated article IDs relevant to the question.
+
+- In **"articles_fr.csv"**:
+  - `id`: an *int32* feature corresponding to a unique ID number for the article.
+  - `article`: a *string* feature corresponding to the full article.
+  - `code`: a *string* feature corresponding to the law code to which the article belongs.
+  - `article_no`: a *string* feature corresponding to the article number in the code.
+  - `description`: a *string* feature corresponding to the concatenated headings of the article.
+  - `law_type`: a *string* feature whose value is either *"regional"* or *"national"*.
 
 ### Data Splits
 
@@ -112,31 +113,31 @@ This dataset is split into train/test set. Number of questions in each set is gi
 
 ### Curation Rationale
 
-[More Information Needed]
+The dataset is intended to be used by researchers to build and evaluate models on retrieving law articles relevant to an input legal question. It should not be regarded as a reliable source of legal information at this point in time, as both the questions and articles correspond to an outdated version of the Belgian law from May 2021 (time of dataset collection). In the latter case, the user is advised to consult daily updated official legal resources (e.g., the Belgian Official Gazette).
 
 ### Source Data
 
 #### Initial Data Collection and Normalization
 
-[More Information Needed]
+BSARD was created in four stages: (i) compiling a large corpus of Belgian law articles, (ii) gathering legal questions with references to relevant law articles, (iii) refining these questions, and (iv) matching the references to the corresponding articles from the corpus.
 
 #### Who are the source language producers?
 
-[More Information Needed]
+Speakers were not directly approached for inclusion in this dataset and thus could not be asked for demographic information. Questions were collected, anonimyzed, and reformulated by [Droits Quotidiens](https://www.droitsquotidiens.be/fr/equipe). Therefore, no direct information about the speakers’ age and gender distribution, or socioeconomic status is available. However, it is expected that most, but not all, of the speakers are adults (18+ years), speak French as a native language, and live in Wallonia or Brussels-Capital region.
 
 ### Annotations
 
 #### Annotation process
 
-[More Information Needed]
+Each year, [Droits Quotidiens](https://www.droitsquotidiens.be/fr/equipe), a Belgian organization whose mission is to clarify the law for laypeople, receives and collects around 4,000 emails from Belgian citizens asking for advice on a personal legal issue. In practice, their legal clarification process consists of four steps. First, they identify the most frequently asked questions on a common legal issue. Then, they define a new anonymized "model" question on that issue expressed in natural language terms, i.e., as close as possible as if a layperson had asked it. Next, they search the Belgian law for articles that help answer the model question and reference them.
 
 #### Who are the annotators?
 
-[More Information Needed]
+A total of six Belgian jurists from [Droits Quotidiens](https://www.droitsquotidiens.be/fr/equipe) contributed to annotating the questions. All have a law degree from a Belgian university and years of experience in providing legal advice and clarifications of the law. They range in age from 30-60 years, including one man and five women, gave their ethnicity as white European, speak French as a native language, and represent upper middle class based on income levels.
 
 ### Personal and Sensitive Information
 
-[More Information Needed]
+The questions represent informal, asynchronous, edited, written language that does not exceed 265 words. None of them contained hateful, aggressive, or inappropriate language as they were all reviewed and reworded by Droits Quotidiens to be neutral, anonymous, and comprehensive. The legal articles represent strong, formal, written language that can contain up to 39,570 words.
 
 ## Considerations for Using the Data
 
@@ -162,10 +163,11 @@ The dataset was created by Antoine Louis during work done at the Law & Tech lab
 
 ### Licensing Information
 
-BSARD is licensed under the [Creative Commons Attribution 4.0 (CC BY-NC-SA 4.0) license](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+BSARD is licensed under the [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/).
 
 ### Citation Information
 
+```
 @inproceedings{louis2021statutory,
   author    = {Antoine Louis and
                Gerasimos Spanakis},
@@ -182,4 +184,4 @@ BSARD is licensed under the [Creative Commons Attribution 4.0 (CC BY-NC-SA 4.0)
 
 ### Contributions
 
-Thanks to [@antoiloui](https://github.com/antoiloui) for adding this dataset.
+Thanks to [@antoiloui](https://github.com/antoiloui) for adding this dataset.