README.md

metadata

annotations_creators:
  - crowdsourced
language_creators:
  - found
languages:
  - en
licenses:
  - ms-pl
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - question-answering
task_ids:
  - extractive-qa

Dataset Card for Microsoft Research Sequential Question Answering

Table of Contents

• Dataset Card for Microsoft Research Sequential Question Answering
  • Table of Contents
  • Dataset Description
    • Dataset Summary
    • Supported Tasks and Leaderboards
    • Languages
  • Dataset Structure
    • Data Instances
    • Data Fields
    • Data Splits
  • Dataset Creation
    • Curation Rationale
    • Source Data
      • Initial Data Collection and Normalization
      • Who are the source language producers?
    • Annotations
      • Annotation process
      • Who are the annotators?
    • Personal and Sensitive Information
  • Considerations for Using the Data
    • Social Impact of Dataset
    • Discussion of Biases
    • Other Known Limitations
  • Additional Information
    • Dataset Curators
    • Licensing Information
    • Citation Information

Dataset Description

• Homepage:Microsoft Research Sequential Question Answering (SQA) Dataset
• Repository:
• Paper:https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/acl17-dynsp.pdf
• Leaderboard:
• Point of Contact:
  • Scott Wen-tau Yih [email protected]
  • Mohit Iyyer [email protected]
  • Ming-Wei Chang [email protected]

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.

• Panupong Pasupat, Percy Liang. "Compositional Semantic Parsing on Semi-Structured Tables" ACL-2015. http://www-nlp.stanford.edu/software/sempre/wikitable/

Supported Tasks and Leaderboards

[More Information Needed]

Languages

English

Dataset Structure

Data Instances

[More Information Needed]

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

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

[More Information Needed]