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--- |
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license: cc-by-sa-4.0 |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: turns |
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list: |
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- name: id |
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dtype: int64 |
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- name: ques_type_id |
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dtype: int64 |
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- name: question-type |
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dtype: string |
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- name: description |
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dtype: string |
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- name: entities_in_utterance |
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list: string |
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- name: relations |
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list: string |
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- name: type_list |
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list: string |
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- name: speaker |
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dtype: string |
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- name: utterance |
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dtype: string |
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- name: all_entities |
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list: string |
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- name: active_set |
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list: string |
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- name: sec_ques_sub_type |
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dtype: int64 |
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- name: sec_ques_type |
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dtype: int64 |
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- name: set_op_choice |
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dtype: int64 |
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- name: is_inc |
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dtype: int64 |
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- name: count_ques_sub_type |
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dtype: int64 |
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- name: count_ques_type |
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dtype: int64 |
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- name: is_incomplete |
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dtype: int64 |
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- name: inc_ques_type |
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dtype: int64 |
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- name: set_op |
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dtype: int64 |
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- name: bool_ques_type |
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dtype: int64 |
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- name: entities |
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list: string |
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- name: clarification_step |
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dtype: int64 |
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- name: gold_actions |
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list: |
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list: string |
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- name: is_spurious |
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dtype: bool |
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- name: masked_verbalized_answer |
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dtype: string |
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- name: parsed_active_set |
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list: string |
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- name: sparql_query |
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dtype: string |
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- name: verbalized_all_entities |
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list: string |
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- name: verbalized_answer |
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dtype: string |
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- name: verbalized_entities_in_utterance |
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list: string |
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- name: verbalized_gold_actions |
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list: |
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list: string |
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- name: verbalized_parsed_active_set |
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list: string |
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- name: verbalized_sparql_query |
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dtype: string |
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- name: verbalized_triple |
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dtype: string |
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- name: verbalized_type_list |
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list: string |
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splits: |
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- name: train |
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num_bytes: 6815016095 |
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num_examples: 152391 |
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- name: test |
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num_bytes: 1007873839 |
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num_examples: 27797 |
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- name: validation |
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num_bytes: 692344634 |
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num_examples: 16813 |
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download_size: 2406342185 |
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dataset_size: 8515234568 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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- split: validation |
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path: data/validation-* |
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task_categories: |
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- conversational |
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- question-answering |
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tags: |
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- qa |
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- knowledge-graph |
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- sparql |
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- multi-hop |
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language: |
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- en |
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--- |
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# Dataset Card for CSQA-SPARQLtoText |
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## Table of Contents |
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- [Dataset Card for CSQA-SPARQLtoText](#dataset-card-for-csqa-sparqltotext) |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported tasks](#supported-tasks) |
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- [Knowledge based question-answering](#knowledge-based-question-answering) |
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- [SPARQL queries and natural language questions](#sparql-queries-and-natural-language-questions) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Types of questions](#types-of-questions) |
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- [Data splits](#data-splits) |
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- [JSON fields](#json-fields) |
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- [Original fields](#original-fields) |
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- [New fields](#new-fields) |
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- [Verbalized fields](#verbalized-fields) |
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- [Format of the SPARQL queries](#format-of-the-sparql-queries) |
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- [Additional Information](#additional-information) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [This version of the corpus (with SPARQL queries)](#this-version-of-the-corpus-with-sparql-queries) |
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- [Original corpus (CSQA)](#original-corpus-csqa) |
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- [CARTON](#carton) |
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## Dataset Description |
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- **Paper:** [SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications (AACL-IJCNLP 2022)](https://aclanthology.org/2022.aacl-main.11/) |
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- **Point of Contact:** Gwénolé Lecorvé |
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### Dataset Summary |
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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. |
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The original data has been post-processing as follows: |
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1. Verbalization templates were applied on the answers and their entities were verbalized (replaced by their label in Wikidata) |
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2. Questions were parsed using the CARTON algorithm to produce a sequence of action in a specific grammar |
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3. Sequence of actions were mapped to SPARQL queries and entities were verbalized (replaced by their label in Wikidata) |
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### Supported tasks |
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- Knowledge-based question-answering |
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- Text-to-SPARQL conversion |
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#### Knowledge based question-answering |
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Below is an example of dialogue: |
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- Q1: Which occupation is the profession of Edmond Yernaux ? |
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- A1: politician |
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- Q2: Which collectable has that occupation as its principal topic ? |
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- A2: Notitia Parliamentaria, An History of the Counties, etc. |
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#### SPARQL queries and natural language questions |
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```SQL |
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SELECT DISTINCT ?x WHERE |
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{ ?x rdf:type ontology:occupation . resource:Edmond_Yernaux property:occupation ?x } |
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``` |
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is equivalent to: |
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```txt |
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Which occupation is the profession of Edmond Yernaux ? |
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``` |
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### Languages |
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- English |
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## Dataset Structure |
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The corpus follows the global architecture from the original version of CSQA (https://amritasaha1812.github.io/CSQA/). |
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There is one directory of the train, dev, and test sets, respectively. |
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Dialogues are stored in separate directories, 100 dialogues per directory. |
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Finally, each dialogue is stored in a JSON file as a list of turns. |
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### Types of questions |
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Comparison of question types compared to related datasets: |
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| | | [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) | |
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|--------------------------|-----------------|:---------------:|:------:|:-----------:|:----:|:---------:| |
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| **Number of triplets in query** | 1 | ✓ | ✓ | ✓ | ✓ | ✓ | |
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| | 2 | | ✓ | ✓ | ✓ | ✓ | |
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| | More | | | ✓ | ✓ | ✓ | |
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| **Logical connector between triplets** | Conjunction | ✓ | ✓ | ✓ | ✓ | ✓ | |
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| | Disjunction | | | | ✓ | ✓ | |
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| | Exclusion | | | | ✓ | ✓ | |
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| **Topology of the query graph** | Direct | ✓ | ✓ | ✓ | ✓ | ✓ | |
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| | Sibling | | ✓ | ✓ | ✓ | ✓ | |
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| | Chain | | ✓ | ✓ | ✓ | ✓ | |
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| | Mixed | | | ✓ | | ✓ | |
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| | Other | | ✓ | ✓ | ✓ | ✓ | |
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| **Variable typing in the query** | None | ✓ | ✓ | ✓ | ✓ | ✓ | |
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| | Target variable | | ✓ | ✓ | ✓ | ✓ | |
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| | Internal variable | | ✓ | ✓ | ✓ | ✓ | |
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| **Comparisons clauses** | None | ✓ | ✓ | ✓ | ✓ | ✓ | |
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| | String | | | ✓ | | ✓ | |
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| | Number | | | ✓ | ✓ | ✓ | |
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| | Date | | | ✓ | | ✓ | |
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| **Superlative clauses** | No | ✓ | ✓ | ✓ | ✓ | ✓ | |
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| | Yes | | | | ✓ | | |
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| **Answer type** | Entity (open) | ✓ | ✓ | ✓ | ✓ | ✓ | |
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| | Entity (closed) | | | | ✓ | ✓ | |
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| | Number | | | ✓ | ✓ | ✓ | |
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| | Boolean | | ✓ | ✓ | ✓ | ✓ | |
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| **Answer cardinality** | 0 (unanswerable) | | | ✓ | | ✓ | |
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| | 1 | ✓ | ✓ | ✓ | ✓ | ✓ | |
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| | More | | ✓ | ✓ | ✓ | ✓ | |
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| **Number of target variables** | 0 (⇒ ASK verb) | | ✓ | ✓ | ✓ | ✓ | |
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| | 1 | ✓ | ✓ | ✓ | ✓ | ✓ | |
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| | 2 | | | ✓ | | ✓ | |
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| **Dialogue context** | Self-sufficient | ✓ | ✓ | ✓ | ✓ | ✓ | |
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| | Coreference | | | | ✓ | ✓ | |
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| | Ellipsis | | | | ✓ | ✓ | |
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| **Meaning** | Meaningful | ✓ | ✓ | ✓ | ✓ | ✓ | |
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| | Non-sense | | | | | ✓ | |
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### Data splits |
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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. |
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| | Train | Validation | Test | |
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| --------------------- | ---------- | ---------- | ---------- | |
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| Questions | 1.5M | 167K | 260K | |
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| Dialogues | 152K | 17K | 28K | |
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| NL question per query | 1 | |
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| Characters per query | 163 (± 100) | |
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| Tokens per question | 10 (± 4) | |
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### JSON fields |
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Each turn of a dialogue contains the following fields: |
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#### Original fields |
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* `ques_type_id`: ID corresponding to the question utterance |
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* `description`: Description of type of question |
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* `relations`: ID's of predicates used in the utterance |
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* `entities_in_utterance`: ID's of entities used in the question |
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* `speaker`: The nature of speaker: `SYSTEM` or `USER` |
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* `utterance`: The utterance: either the question, clarification or response |
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* `active_set`: A regular expression which identifies the entity set of answer list |
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* `all_entities`: List of ALL entities which constitute the answer of the question |
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* `question-type`: Type of question (broad types used for evaluation as given in the original authors' paper) |
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* `type_list`: List containing entity IDs of all entity parents used in the question |
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#### New fields |
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* `is_spurious`: introduced by CARTON, |
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* `is_incomplete`: either the question is self-sufficient (complete) or it relies on information given by the previous turns (incomplete) |
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* `parsed_active_set`: |
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* `gold_actions`: sequence of ACTIONs as returned by CARTON |
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* `sparql_query`: SPARQL query |
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#### Verbalized fields |
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Fields with `verbalized` in their name are verbalized versions of another fields, ie IDs were replaced by actual words/labels. |
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### Format of the SPARQL queries |
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* Clauses are in random order |
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* Variables names are represented as random letters. The letters change from one turn to another. |
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* Delimiters are spaced |
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## Additional Information |
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### Licensing Information |
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* Content from original dataset: CC-BY-SA 4.0 |
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* New content: CC BY-SA 4.0 |
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### Citation Information |
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#### This version of the corpus (with SPARQL queries) |
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```bibtex |
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@inproceedings{lecorve2022sparql2text, |
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title={SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications}, |
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author={Lecorv\'e, Gw\'enol\'e and Veyret, Morgan and Brabant, Quentin and Rojas-Barahona, Lina M.}, |
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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)}, |
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year={2022} |
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} |
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``` |
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#### Original corpus (CSQA) |
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```bibtex |
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@InProceedings{saha2018complex, |
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title = {Complex {Sequential} {Question} {Answering}: {Towards} {Learning} to {Converse} {Over} {Linked} {Question} {Answer} {Pairs} with a {Knowledge} {Graph}}, |
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volume = {32}, |
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issn = {2374-3468}, |
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url = {https://ojs.aaai.org/index.php/AAAI/article/view/11332}, |
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booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, |
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author = {Saha, Amrita and Pahuja, Vardaan and Khapra, Mitesh and Sankaranarayanan, Karthik and Chandar, Sarath}, |
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month = apr, |
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year = {2018} |
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} |
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``` |
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#### CARTON |
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```bibtex |
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@InProceedings{plepi2021context, |
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author="Plepi, Joan and Kacupaj, Endri and Singh, Kuldeep and Thakkar, Harsh and Lehmann, Jens", |
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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", |
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title="Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs", |
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booktitle="Proceedings of The Semantic Web", |
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year="2021", |
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publisher="Springer International Publishing", |
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pages="356--371", |
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isbn="978-3-030-77385-4" |
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} |
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``` |
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