Dataset Card for CSQA-SPARQLtoText

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

SELECT DISTINCT ?x WHERE
{ ?x rdf:type ontology:occupation . resource:Edmond_Yernaux property:occupation ?x }

is equivalent to:

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	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	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)

@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)

@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

@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"
}