Enriched readme

README.md

@@ -102,3 +102,113 @@ dataset_info:
   download_size: 9637685
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+
+# Dataset Card for WEBNLG-QA
+
+## Dataset Description
+
+- **Paper:** [SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications (AACL-IJCNLP 2022)](https://aclanthology.org/2022.aacl-main.11/)
+- **Point of Contact:** Gwénolé Lecorvé
+
+### Dataset Summary
+
+WEBNLG-QA is a conversational question answering dataset grounded on WEBNLG. It consists in a set of question-answering dialogues (follow-up question-answer pairs) based on short paragraphs of text. Each paragraph is associated a knowledge graph (from WEBNLG). The questions are associated with SPARQL queries.
+
+### Supported tasks
+
+The dataset can be used for:
+* Knowledge-based question-answering
+* SPARQL-to-Text conversion
+
+
+#### Knowledge based question-answering
+
+Below is an example of dialogue:
+- Q1: What is used as an instrument is Sludge Metal or in Post-metal?
+- A1: Singing, Synthesizer
+- Q2: And what about Sludge Metal in particular?
+- A2: Singing
+- Q3: Does the Year of No Light  album Nord belong to this genre?
+- A3: Yes.
+
+#### SPARQL-to-Text Question Generation
+
+SPARQL-to-Text question generation refers to the task of converting a SPARQL query into a natural language question, eg:
+
+```SQL
+SELECT (COUNT(?country) as ?answer)
+WHERE  { ?country property:member_of resource:Europe .
+                ?country property:population ?n .
+                FILTER ( ?n > 10000000 )
+              }
+```
+
+could be converted into:
+
+```txt
+How many European countries have more than 10 million inhabitants?
+```
+
+## Dataset Structure
+
+### Types of questions
+
+Questions are very diverses, ranging the following feature categories:
+* SPARQL query
+  * Number of triplets: 1, 2, 3+
+  * Logical connector between clauses: conjunction, disjunction, exclusion
+  * Topology of the query graph: direct, sibling, chain, mixed, other
+  * Variable typing: none, target variable, internal variable
+  * Comparison clause: none, string, number, date
+* Answer
+  * Type: entity (open), entity (close list of choices), number, boolean
+  * Number of answers: 0 (unanswerable question), 1, 2+
+  * Number of target variables: 0 ("ASK" verb), 1, 2
+* Contextuality
+  * Dialogue context: self-sufficient question, contains coreference, contains ellipsis
+  * Meaning: meaninful question, non-sense question
+
+
+### Data splits
+
+Text verbalization is only available for a subset of the test set, referred to as *challenge set*. Other sample only contain dialogues in the form of follow-up sparql queries.
+
+|                       | Train      | Validation | Test       |  Challenge   |
+| --------------------- | ---------- | ---------- | ---------- | ------------ |
+| Questions             | 27727      | 3485       | 4179       |  332         |
+| Dialogues             | 1001       | 1264       | 1417       |  100         |
+| NL question per query | 0          | 0          | 0          |  2           |
+| Characters per query  | 129 (± 43) | 131 (± 45) | 122 (± 45) |  113 (± 38)  |
+| Tokens per question   |   -        |  -         |   -        |  8.4 (± 4.5) |
+
+
+## Additional information
+
+### Related datasets
+
+This corpus is part of a set of 5 datasets released for SPARQL-to-Text generation, namely:
+  - Non conversational datasets
+    - [SimpleQuestions](https://huggingface.co/datasets/OrangeInnov/simplequestions-sparqltotext) (from https://github.com/askplatypus/wikidata-simplequestions)
+    - [ParaQA](https://huggingface.co/datasets/OrangeInnov/paraqa-sparqltotext) (from https://github.com/barshana-banerjee/ParaQA)
+    - [LC-QuAD 2.0](https://huggingface.co/datasets/OrangeInnov/lcquad_2.0-sparqltotext) (from http://lc-quad.sda.tech/)
+  - Conversational datasets
+    - [CSQA](https://huggingface.co/datasets/OrangeInnov/csqa-sparqltotext) (from https://amritasaha1812.github.io/CSQA/)
+    - [WebNLQ-QA](https://huggingface.co/datasets/OrangeInnov/webnlg-qa) (derived from https://gitlab.com/shimorina/webnlg-dataset/-/tree/master/release_v3.0)
+
+### Licencing information
+
+* Content from original dataset: CC-BY-SA 4.0
+* New content: CC BY-SA 4.0
+
+
+### Citation information
+
+```bibtex
+@inproceedings{lecorve2022sparql2text,
+  title={SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications},
+  author={Lecorv\'e, Gw\'enol\'e and Veyret, Morgan and Brabant, Quentin and Rojas-Barahona, Lina M.},
+  journal={Proceedings of the Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP)},
+  year={2022}
+}
+```
+