Datasets:
File size: 10,985 Bytes
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---
license: cc-by-sa-4.0
dataset_info:
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dtype: string
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num_examples: 100
download_size: 9637685
dataset_size: 42808841
task_categories:
- conversational
- question-answering
- text-generation
tags:
- qa
- knowledge-graph
- sparql
language:
- en
---
# 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
* 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
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 | 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
#### This dataset
```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}
}
```
#### The underlying corpus WEBNLG 3.0
```bibtex
@inproceedings{castro-ferreira-etal-2020-2020,
title = "The 2020 Bilingual, Bi-Directional {W}eb{NLG}+ Shared Task: Overview and Evaluation Results ({W}eb{NLG}+ 2020)",
author = "Castro Ferreira, Thiago and Gardent, Claire and Ilinykh, Nikolai and van der Lee, Chris and Mille, Simon and Moussallem, Diego and Shimorina, Anastasia",
booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
year = "2020",
pages = "55--76"
}
```
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