metadata

dataset_info:
  features:
    - name: original_nl_question
      dtype: string
    - name: recased_nl_question
      dtype: string
    - name: sparql_query
      dtype: string
    - name: verbalized_sparql_query
      dtype: string
    - name: nl_subject
      dtype: string
    - name: nl_property
      dtype: string
    - name: nl_object
      dtype: string
    - name: nl_answer
      dtype: string
    - name: rdf_subject
      dtype: string
    - name: rdf_property
      dtype: string
    - name: rdf_object
      dtype: string
    - name: rdf_answer
      dtype: string
    - name: rdf_target
      dtype: string
  splits:
    - name: train
      num_bytes: 11403929
      num_examples: 34374
    - name: validation
      num_bytes: 1614051
      num_examples: 4867
    - name: test
      num_bytes: 3304281
      num_examples: 9961
  download_size: 7595264
  dataset_size: 16322261
task_categories:
  - question-answering
  - text-generation
tags:
  - qa
  - knowledge-graph
  - sparql
language:
  - en

Dataset Card for SimpleQuestions-SPARQLtoText

Dataset Card for SimpleQuestions-SPARQLtoText

Dataset Description

Paper: SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications (AACL-IJCNLP 2022)
Point of Contact: Gwénolé Lecorvé

Dataset Summary

Special version of SimpleQuestions with SPARQL queries formatted for the SPARQL-to-Text task.

JSON fields

The original version of SimpleQuestions is a raw text file listing triples and the natural language question. A JSON version has been generated and augmented with the following fields:

rdf_subject, rdf_property, rdf_object: triple in the Wikidata format (IDs)
nl_subject, nl_property, nl_object: triple with labels retrieved from Wikidata. Some entities do not have labels, they are labelled as UNDEFINED_LABEL
sparql_query: SPARQL query with Wikidata IDs
verbalized_sparql_query: SPARQL query with labels
original_nl_question: original natural language question from SimpleQuestions. This is in lower case.
recased_nl_question: Version of original_nl_question where the named entities have been automatically recased based on the labels of the entities.

Format of the SPARQL queries

Randomizing the variables names
Delimiters are spaced

Answerable/unanswerable

Some questions in SimpleQuestions cannot be answered. Hence, it originally comes with 2 versions for the train/valid/test sets: one with all entries, another with the answerable questions only.

Languages

English

Dataset Structure

Types of questions

Comparison of question types compared to related datasets:

		SimpleQuestions	ParaQA	LC-QuAD 2.0	CSQA	WebNLQ-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	34,000	5,000	10,000
NL question per query	1
Characters per query	70 (± 10)
Tokens per question	7.4 (± 2.1)

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 3.0
New content: CC BY-SA 4.0

Citation information

This version of the corpus (with normalized SPARQL queries)

@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 version

@article{bordes2015large,
  title={Large-scale simple question answering with memory networks},
  author={Bordes, Antoine and Usunier, Nicolas and Chopra, Sumit and Weston, Jason},
  journal={arXiv preprint arXiv:1506.02075},
  year={2015}
}

Datasets:

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simplequestions-sparqltotext