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Datasets:
microsoft
/
msr_sqa

Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
English
Size:
10K<n<100K
License:
Dataset card Files Community
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metadata
annotations_creators:
  - crowdsourced
language_creators:
  - found
language:
  - en
license:
  - ms-pl
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - question-answering
task_ids:
  - extractive-qa
paperswithcode_id: null
pretty_name: Microsoft Research Sequential Question Answering
dataset_info:
  features:
    - name: id
      dtype: string
    - name: annotator
      dtype: int32
    - name: position
      dtype: int32
    - name: question
      dtype: string
    - name: question_and_history
      sequence: string
    - name: table_file
      dtype: string
    - name: table_header
      sequence: string
    - name: table_data
      sequence:
        sequence: string
    - name: answer_coordinates
      sequence:
        - name: row_index
          dtype: int32
        - name: column_index
          dtype: int32
    - name: answer_text
      sequence: string
  splits:
    - name: train
      num_bytes: 19732499
      num_examples: 12276
    - name: validation
      num_bytes: 3738331
      num_examples: 2265
    - name: test
      num_bytes: 5105873
      num_examples: 3012
  download_size: 4796932
  dataset_size: 28576703

Dataset Card for Microsoft Research Sequential Question Answering

Table of Contents

Dataset Description

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 (en).

Dataset Structure

Data Instances 

{'id': 'nt-639',
 'annotator': 0,
 'position': 0,
 'question': 'where are the players from?',
 'table_file': 'table_csv/203_149.csv',
 'table_header': ['Pick', 'Player', 'Team', 'Position', 'School'],
 'table_data': [['1',
   'Ben McDonald',
   'Baltimore Orioles',
   'RHP',
   'Louisiana State University'],
  ['2',
   'Tyler Houston',
   'Atlanta Braves',
   'C',
   '"Valley HS (Las Vegas',
   ' NV)"'],
  ['3', 'Roger Salkeld', 'Seattle Mariners', 'RHP', 'Saugus (CA) HS'],
  ['4',
   'Jeff Jackson',
   'Philadelphia Phillies',
   'OF',
   '"Simeon HS (Chicago',
   ' IL)"'],
  ['5', 'Donald Harris', 'Texas Rangers', 'OF', 'Texas Tech University'],
  ['6', 'Paul Coleman', 'Saint Louis Cardinals', 'OF', 'Frankston (TX) HS'],
  ['7', 'Frank Thomas', 'Chicago White Sox', '1B', 'Auburn University'],
  ['8', 'Earl Cunningham', 'Chicago Cubs', 'OF', 'Lancaster (SC) HS'],
  ['9',
   'Kyle Abbott',
   'California Angels',
   'LHP',
   'Long Beach State University'],
  ['10',
   'Charles Johnson',
   'Montreal Expos',
   'C',
   '"Westwood HS (Fort Pierce',
   ' FL)"'],
  ['11',
   'Calvin Murray',
   'Cleveland Indians',
   '3B',
   '"W.T. White High School (Dallas',
   ' TX)"'],
  ['12', 'Jeff Juden', 'Houston Astros', 'RHP', 'Salem (MA) HS'],
  ['13', 'Brent Mayne', 'Kansas City Royals', 'C', 'Cal State Fullerton'],
  ['14',
   'Steve Hosey',
   'San Francisco Giants',
   'OF',
   'Fresno State University'],
  ['15',
   'Kiki Jones',
   'Los Angeles Dodgers',
   'RHP',
   '"Hillsborough HS (Tampa',
   ' FL)"'],
  ['16', 'Greg Blosser', 'Boston Red Sox', 'OF', 'Sarasota (FL) HS'],
  ['17', 'Cal Eldred', 'Milwaukee Brewers', 'RHP', 'University of Iowa'],
  ['18',
   'Willie Greene',
   'Pittsburgh Pirates',
   'SS',
   '"Jones County HS (Gray',
   ' GA)"'],
  ['19', 'Eddie Zosky', 'Toronto Blue Jays', 'SS', 'Fresno State University'],
  ['20', 'Scott Bryant', 'Cincinnati Reds', 'OF', 'University of Texas'],
  ['21', 'Greg Gohr', 'Detroit Tigers', 'RHP', 'Santa Clara University'],
  ['22',
   'Tom Goodwin',
   'Los Angeles Dodgers',
   'OF',
   'Fresno State University'],
  ['23', 'Mo Vaughn', 'Boston Red Sox', '1B', 'Seton Hall University'],
  ['24', 'Alan Zinter', 'New York Mets', 'C', 'University of Arizona'],
  ['25', 'Chuck Knoblauch', 'Minnesota Twins', '2B', 'Texas A&M University'],
  ['26', 'Scott Burrell', 'Seattle Mariners', 'RHP', 'Hamden (CT) HS']],
 'answer_coordinates': {'row_index': [0,
   1,
   2,
   3,
   4,
   5,
   6,
   7,
   8,
   9,
   10,
   11,
   12,
   13,
   14,
   15,
   16,
   17,
   18,
   19,
   20,
   21,
   22,
   23,
   24,
   25],
  'column_index': [4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4,
   4]},
 'answer_text': ['Louisiana State University',
  'Valley HS (Las Vegas, NV)',
  'Saugus (CA) HS',
  'Simeon HS (Chicago, IL)',
  'Texas Tech University',
  'Frankston (TX) HS',
  'Auburn University',
  'Lancaster (SC) HS',
  'Long Beach State University',
  'Westwood HS (Fort Pierce, FL)',
  'W.T. White High School (Dallas, TX)',
  'Salem (MA) HS',
  'Cal State Fullerton',
  'Fresno State University',
  'Hillsborough HS (Tampa, FL)',
  'Sarasota (FL) HS',
  'University of Iowa',
  'Jones County HS (Gray, GA)',
  'Fresno State University',
  'University of Texas',
  'Santa Clara University',
  'Fresno State University',
  'Seton Hall University',
  'University of Arizona',
  'Texas A&M University',
  'Hamden (CT) HS']}

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

train test
N. examples 14541 3012

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

Microsoft Research Data License Agreement.

Citation Information 

@inproceedings{iyyer-etal-2017-search,
    title = "Search-based Neural Structured Learning for Sequential Question Answering",
    author = "Iyyer, Mohit  and
      Yih, Wen-tau  and
      Chang, Ming-Wei",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P17-1167",
    doi = "10.18653/v1/P17-1167",
    pages = "1821--1831",
}

Contributions

Thanks to @mattbui for adding this dataset.