Update README.md

README.md

@@ -1,3 +1,56 @@
 ---
 license: cc-by-4.0
+task_categories:
+- table-question-answering
+- text2text-generation
+language:
+- en
+tags:
+- sparql
+- semantic web
+- knowledge graph
+- knowledge base
+- dbpedia
+pretty_name: VQuAnDa - Verbalization Question Answering Dataset
+size_categories:
+- 1K<n<10K
 ---
+# VQuAnDa - Verbalization Question Answering Dataset
+## Background
+[VQuAnDa](https://dl.acm.org/doi/abs/10.1007/978-3-030-49461-2_31) is knowledge base QA dataset based on
+[LC-QuAD](https://github.com/AskNowQA/LC-QuAD) which uses
+[DBpedia v04.16](https://wiki.dbpedia.org/dbpedia-version-2016-04) as the target KB.
+
+This QA task consists of two components:
+  1. A Text2Sparql task where a natural language query is translated to a SPARQL query.
+  2. An RDF triple to verbalized answer task where the knowledge base query result must be translated back into natural language.
+
+The dataset is in JSON format, and it contains 5000 examples (4000 train/1000 test).
+An example is shown below. The query result is surrounded by brackets `[]` in the verbalized answer.
+```
+{
+  "uid"               :                                              "3508"
+  "question"          :       "How many shows are aired on Comedy Central?"
+  "verbalized_answer" : "There are [73] television shows broadcasted by..."
+  "query"             : "SELECT DISTINCT COUNT(?uri) WHERE {?uri <http:..."
+}
+```
+
+## Baseline models
+Alongside the dataset, the authors provide some baseline models.
+[Here](https://github.com/endrikacupaj/VQUANDA-Baseline-Models) you can find the baseline implementations and instructions for how to run them.
+
+## License
+The dataset is under [Attribution 4.0 International (CC BY 4.0)](LICENSE)
+
+## Cite
+```
+@InProceedings{kacupaj2020vquanda,
+    title={VQuAnDa: Verbalization QUestion ANswering DAtaset},
+    author={Kacupaj, Endri and Zafar, Hamid and Lehmann, Jens and Maleshkova, Maria},
+    booktitle={The Semantic Web},
+    pages={531--547},
+    year={2020},
+    publisher={Springer International Publishing},
+}
+```