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--- |
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annotations_creators: |
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- crowdsourced |
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language_creators: |
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- found |
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languages: |
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- en |
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licenses: |
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- ms-pl |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 10K<n<100K |
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source_datasets: |
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- original |
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task_categories: |
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- question-answering |
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task_ids: |
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- extractive-qa |
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--- |
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# Dataset Card for Microsoft Research Sequential Question Answering |
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## Table of Contents |
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- [Dataset Card for Microsoft Research Sequential Question Answering](#dataset-card-for-microsoft-research-sequential-question-answering) |
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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 and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) |
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- [Who are the source language producers?](#who-are-the-source-language-producers) |
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- [Annotations](#annotations) |
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- [Annotation process](#annotation-process) |
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- [Who are the annotators?](#who-are-the-annotators) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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## Dataset Description |
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- **Homepage:[Microsoft Research Sequential Question Answering (SQA) Dataset](https://msropendata.com/datasets/b25190ed-0f59-47b1-9211-5962858142c2)** |
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- **Repository:** |
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- **Paper:[https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/acl17-dynsp.pdf](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/acl17-dynsp.pdf)** |
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- **Leaderboard:** |
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- **Point of Contact:** |
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- Scott Wen-tau Yih scottyih@microsoft.com |
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- Mohit Iyyer m.iyyer@gmail.com |
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- Ming-Wei Chang minchang@microsoft.com |
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### Dataset Summary |
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Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. |
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We created SQA by asking crowdsourced workers to decompose 2,022 questions from WikiTableQuestions (WTQ)*, which contains highly-compositional questions about tables from Wikipedia. We had three workers decompose each WTQ question, resulting in a dataset of 6,066 sequences that contain 17,553 questions in total. Each question is also associated with answers in the form of cell locations in the tables. |
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- Panupong Pasupat, Percy Liang. "Compositional Semantic Parsing on Semi-Structured Tables" ACL-2015. |
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[http://www-nlp.stanford.edu/software/sempre/wikitable/](http://www-nlp.stanford.edu/software/sempre/wikitable/) |
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### Supported Tasks and Leaderboards |
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[More Information Needed] |
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### Languages |
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English |
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## Dataset Structure |
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### Data Instances |
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[More Information Needed] |
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### Data Fields |
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- `id` (`str`): question sequence id (the id is consistent with those in WTQ) |
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- `annotator` (`int`): `0`, `1`, `2` (the 3 annotators who annotated the question intent) |
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- `position` (`int`): the position of the question in the sequence |
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- `question` (`str`): the question given by the annotator |
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- `table_file` (`str`): the associated table |
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- `table_header` (`List[str]`): a list of headers in the table |
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- `table_data` (`List[List[str]]`): 2d array of data in the table |
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- `answer_coordinates` (`List[Dict]`): the table cell coordinates of the answers (0-based, where 0 is the first row after the table header) |
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- `row_index` |
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- `column_index` |
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- `answer_text` (`List[str]`): the content of the answer cells |
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Note that some text fields may contain Tab or LF characters and thus start with quotes. |
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It is recommended to use a CSV parser like the Python CSV package to process the data. |
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### Data Splits |
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[More Information Needed] |
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## Dataset Creation |
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### Curation Rationale |
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[More Information Needed] |
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### Source Data |
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#### Initial Data Collection and Normalization |
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[More Information Needed] |
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#### Who are the source language producers? |
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[More Information Needed] |
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### Annotations |
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#### Annotation process |
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[More Information Needed] |
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#### Who are the annotators? |
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[More Information Needed] |
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### Personal and Sensitive Information |
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[More Information Needed] |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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[More Information Needed] |
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### Discussion of Biases |
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[More Information Needed] |
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### Other Known Limitations |
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[More Information Needed] |
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## Additional Information |
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### Dataset Curators |
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[More Information Needed] |
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### Licensing Information |
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[More Information Needed] |
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### Citation Information |
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[More Information Needed] |
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