Orange/webnlg-qa

Dataset Card for WEBNLG-QA

Dataset Description

• —Paper: SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications (AACL-IJCNLP 2022)
• —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:

SELECT (COUNT(?country) as ?answer)
WHERE  { ?country property:member_of resource:Europe .
                ?country property:population ?n .
                FILTER ( ?n > 10000000 )
              }

could be converted into:

How many European countries have more than 10 million inhabitants?

Dataset Structure

Types of questions

Comparison of question types compared to related datasets:

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.

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 (from https://github.com/askplatypus/wikidata-simplequestions)
• —ParaQA (from https://github.com/barshana-banerjee/ParaQA)
• —LC-QuAD 2.0 (from http://lc-quad.sda.tech/)
• —Conversational datasets
• —CSQA (from https://amritasaha1812.github.io/CSQA/)
• —WebNLQ-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

@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

@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"
}