Dataset:
msr_sqa

Task Categories: question-answering
Languages: en
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: ms-pl
Language Creators: found
Annotations Creators: crowdsourced
Source Datasets: original

Dataset Card for Microsoft Research Sequential Question Answering

Dataset Summary

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.

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.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

English

Dataset Structure

Data Instances

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Data Fields

  • id (str): question sequence id (the id is consistent with those in WTQ)
  • annotator (int): 0, 1, 2 (the 3 annotators who annotated the question intent)
  • position (int): the position of the question in the sequence
  • question (str): the question given by the annotator
  • table_file (str): the associated table
  • table_header (List[str]): a list of headers in the table
  • table_data (List[List[str]]): 2d array of data in the table
  • answer_coordinates (List[Dict]): the table cell coordinates of the answers (0-based, where 0 is the first row after the table header)
    • row_index
    • column_index
  • answer_text (List[str]): the content of the answer cells

Note that some text fields may contain Tab or LF characters and thus start with quotes. It is recommended to use a CSV parser like the Python CSV package to process the data.

Data Splits

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Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

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Contributions

Thanks to @mattbui for adding this dataset.

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