Enriched README

README.md

@@ -101,4 +101,258 @@ configs:
     path: data/test-*
   - split: validation
     path: data/validation-*
+task_categories:
+- conversational
+- question-answering
+tags:
+- qa
+- knowledge-graph
+- sparql
+- multi-hop
+language:
+- en
 ---
+
+# Dataset Card for CSQA-SPARQLtoText
+
+## Table of Contents
+- [Dataset Card for CSQA-SPARQLtoText](#dataset-card-for-csqa-sparqltotext)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported tasks](#supported-tasks)
+      - [Knowledge based question-answering](#knowledge-based-question-answering)
+      - [SPARQL queries and natural language questions](#sparql-queries-and-natural-language-questions)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Types of questions](#types-of-questions)
+    - [Data splits](#data-splits)
+    - [JSON fields](#json-fields)
+      - [Original fields](#original-fields)
+      - [New fields](#new-fields)
+      - [Verbalized fields](#verbalized-fields)
+    - [Format of the SPARQL queries](#format-of-the-sparql-queries)
+  - [Additional Information](#additional-information)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+      - [This version of the corpus (with SPARQL queries)](#this-version-of-the-corpus-with-sparql-queries)
+      - [Original corpus (CSQA)](#original-corpus-csqa)
+      - [CARTON](#carton)
+
+
+## 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
+
+CSQA corpus (Complex Sequential Question-Answering, see https://amritasaha1812.github.io/CSQA/) is a large corpus for conversational knowledge-based question answering. The version here is augmented with various fields to make it easier to run specific tasks, especially SPARQL-to-text conversion.
+
+The original data has been post-processing as follows:
+
+1. Verbalization templates were applied on the answers and their entities were verbalized (replaced by their label in Wikidata)
+
+2. Questions were parsed using the CARTON algorithm to produce a sequence of action in a specific grammar
+
+3. Sequence of actions were mapped to SPARQL queries and entities were verbalized (replaced by their label in Wikidata)
+
+### Supported tasks
+
+- Knowledge-based question-answering
+- Text-to-SPARQL conversion
+
+#### Knowledge based question-answering
+
+Below is an example of dialogue:
+- Q1: Which occupation is the profession of Edmond Yernaux ?
+- A1: politician
+- Q2: Which collectable has that occupation as its principal topic ?
+- A2: Notitia Parliamentaria, An History of the Counties, etc.
+
+#### SPARQL queries and natural language questions
+
+```SQL
+SELECT DISTINCT ?x WHERE
+{ ?x rdf:type ontology:occupation . resource:Edmond_Yernaux property:occupation ?x }
+```
+
+is equivalent to:
+
+```txt
+Which occupation is the profession of Edmond Yernaux ?
+```
+
+### Languages
+
+- English
+
+## Dataset Structure
+
+
+The corpus follows the global architecture from the original version of CSQA (https://amritasaha1812.github.io/CSQA/).
+
+There is one directory of the train, dev, and test sets, respectively.
+
+Dialogues are stored in separate directories, 100 dialogues per directory.
+
+Finally, each dialogue is stored in a JSON file as a list of turns.
+
+### Types of questions
+
+Comparison of question types compared to related datasets:
+
+|                          |                 | [SimpleQuestions](https://huggingface.co/datasets/OrangeInnov/simplequestions-sparqltotext) | [ParaQA](https://huggingface.co/datasets/OrangeInnov/paraqa-sparqltotext) | [LC-QuAD 2.0](https://huggingface.co/datasets/OrangeInnov/lcquad_2.0-sparqltotext) | [CSQA](https://huggingface.co/datasets/OrangeInnov/csqa-sparqltotext) | [WebNLQ-QA](https://huggingface.co/datasets/OrangeInnov/webnlg-qa) |
+|--------------------------|-----------------|:---------------:|:------:|:-----------:|:----:|:---------:|
+| **Number of triplets in query**   | 1               |        ✓        |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | 2               |                 |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | More            |                 |        |      ✓      |   ✓  |     ✓     |
+| **Logical connector between triplets**    | Conjunction     |        ✓        |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | Disjunction     |                 |        |             |   ✓  |     ✓     |
+|                          | Exclusion       |                 |        |             |   ✓  |     ✓     |
+| **Topology of the query graph**             | Direct          |        ✓        |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | Sibling         |                 |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | Chain           |                 |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | Mixed           |                 |        |      ✓      |      |     ✓     |
+|                          | Other           |                 |    ✓   |      ✓      |   ✓  |     ✓     |
+| **Variable typing in the query**      | None            |        ✓        |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | Target variable     |                 |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | Internal variable   |                 |    ✓   |      ✓      |   ✓  |     ✓     |
+| **Comparisons clauses**          | None            |        ✓        |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | String          |                 |        |      ✓      |      |     ✓     |
+|                          | Number          |                 |        |      ✓      |   ✓  |     ✓     |
+|                          | Date            |                 |        |      ✓      |      |     ✓     |
+| **Superlative clauses**          | No              |        ✓        |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | Yes             |                 |        |             |   ✓  |           |
+| **Answer type**          | Entity (open)   |        ✓        |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | Entity (closed) |                 |        |             |   ✓  |     ✓     |
+|                          | Number          |                 |        |      ✓      |   ✓  |     ✓     |
+|                          | Boolean         |                 |    ✓   |      ✓      |   ✓  |     ✓     |
+| **Answer cardinality**   | 0 (unanswerable)   |                 |        |      ✓      |      |     ✓     |
+|                          | 1               |        ✓        |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | More            |                 |    ✓   |      ✓      |   ✓  |     ✓     |
+| **Number of target variables** | 0 (⇒ ASK verb)       |                 |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | 1               |        ✓        |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | 2               |                 |        |      ✓      |      |     ✓     |
+| **Dialogue context**     | Self-sufficient |        ✓        |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | Coreference     |                 |        |             |   ✓  |     ✓     |
+|                          | Ellipsis        |                 |        |             |   ✓  |     ✓     |
+| **Meaning**              | Meaningful      |        ✓        |    ✓   |      ✓      |   ✓  |     ✓     |
+|                          | Non-sense       |                 |        |             |      |     ✓     |
+
+
+
+### 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       |
+| --------------------- | ---------- | ---------- | ---------- |
+| Questions             | 1.5M      | 167K       | 260K       |
+| Dialogues             | 152K       | 17K       | 28K       |
+| NL question per query | 1          |
+| Characters per query  | 163 (± 100) |
+| Tokens per question   | 10 (± 4)     |
+
+### JSON fields
+
+Each turn of a dialogue contains the following fields:
+
+#### Original fields
+
+* `ques_type_id`: ID corresponding to the question utterance
+
+* `description`: Description of type of question
+
+* `relations`: ID's of predicates used in the utterance
+
+* `entities_in_utterance`: ID's of entities used in the question
+
+* `speaker`: The nature of speaker: `SYSTEM` or `USER`
+
+* `utterance`: The utterance: either the question, clarification or response
+
+* `active_set`: A regular expression which identifies the entity set of answer list
+
+* `all_entities`: List of ALL entities which constitute the answer of the question
+
+* `question-type`: Type of question (broad types used for evaluation as given in the original authors' paper)
+
+* `type_list`: List containing entity IDs of all entity parents used in the question
+
+#### New fields
+
+* `is_spurious`: introduced by CARTON, 
+
+* `is_incomplete`: either the question is self-sufficient (complete) or it relies on information given by the previous turns (incomplete)
+
+* `parsed_active_set`: 
+
+* `gold_actions`: sequence of ACTIONs as returned by CARTON
+
+* `sparql_query`: SPARQL query
+
+#### Verbalized fields
+
+Fields with `verbalized` in their name are verbalized versions of another fields, ie IDs were replaced by actual words/labels.
+
+### Format of the SPARQL queries
+
+* Clauses are in random order
+
+* Variables names are represented as random letters. The letters change from one turn to another.
+
+* Delimiters are spaced
+
+
+## Additional Information
+
+### Licensing Information
+
+* Content from original dataset: CC-BY-SA 4.0
+
+* New content: CC BY-SA 4.0
+
+### Citation Information
+
+
+#### This version of the corpus (with SPARQL queries)
+
+```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}
+}
+```
+
+#### Original corpus (CSQA)
+
+```bibtex
+@InProceedings{saha2018complex,
+	title = {Complex {Sequential} {Question} {Answering}: {Towards} {Learning} to {Converse} {Over} {Linked} {Question} {Answer} {Pairs} with a {Knowledge} {Graph}},
+	volume = {32},
+	issn = {2374-3468},
+	url = {https://ojs.aaai.org/index.php/AAAI/article/view/11332},
+	booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
+	author = {Saha, Amrita and Pahuja, Vardaan and Khapra, Mitesh and Sankaranarayanan, Karthik and Chandar, Sarath},
+	month = apr,
+	year = {2018}
+}
+```
+
+#### CARTON
+
+```bibtex
+@InProceedings{plepi2021context,
+	author="Plepi, Joan and Kacupaj, Endri and Singh, Kuldeep and Thakkar, Harsh and Lehmann, Jens",
+	editor="Verborgh, Ruben and Hose, Katja and Paulheim, Heiko and Champin, Pierre-Antoine and Maleshkova, Maria and Corcho, Oscar and Ristoski, Petar and Alam, Mehwish",
+	title="Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs",
+	booktitle="Proceedings of The Semantic Web",
+	year="2021",
+	publisher="Springer International Publishing",
+	pages="356--371",
+	isbn="978-3-030-77385-4"
+}
+```