metadata

license: cc-by-sa-4.0
dataset_info:
  features:
    - name: category
      dtype: string
    - name: size
      dtype: int32
    - name: id
      dtype: string
    - name: eid
      dtype: string
    - name: original_triple_sets
      list:
        - name: subject
          dtype: string
        - name: property
          dtype: string
        - name: object
          dtype: string
    - name: modified_triple_sets
      list:
        - name: subject
          dtype: string
        - name: property
          dtype: string
        - name: object
          dtype: string
    - name: shape
      dtype: string
    - name: shape_type
      dtype: string
    - name: lex
      sequence:
        - name: comment
          dtype: string
        - name: lid
          dtype: string
        - name: text
          dtype: string
        - name: lang
          dtype: string
    - name: test_category
      dtype: string
    - name: dbpedia_links
      sequence: string
    - name: links
      sequence: string
    - name: graph
      list:
        list: string
    - name: main_entity
      dtype: string
    - name: mappings
      list:
        - name: modified
          dtype: string
        - name: readable
          dtype: string
        - name: graph
          dtype: string
    - name: dialogue
      list:
        - name: question
          list:
            - name: source
              dtype: string
            - name: text
              dtype: string
        - name: graph_query
          dtype: string
        - name: readable_query
          dtype: string
        - name: graph_answer
          list: string
        - name: readable_answer
          list: string
        - name: type
          list: string
  splits:
    - name: train
      num_bytes: 33200723
      num_examples: 10016
    - name: validation
      num_bytes: 4196972
      num_examples: 1264
    - name: test
      num_bytes: 4990595
      num_examples: 1417
    - name: challenge
      num_bytes: 420551
      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)
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:

		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	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 (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"
}