| --- |
| 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-description) |
| - [Dataset Summary](#dataset-summary) |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
| - [Languages](#languages) |
| - [Dataset Structure](#dataset-structure) |
| - [Data Instances](#data-instances) |
| - [Data Fields](#data-fields) |
| - [Data Splits](#data-splits) |
| - [Dataset Creation](#dataset-creation) |
| - [Curation Rationale](#curation-rationale) |
| - [Source Data](#source-data) |
| - [Annotations](#annotations) |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) |
| - [Considerations for Using the Data](#considerations-for-using-the-data) |
| - [Social Impact of Dataset](#social-impact-of-dataset) |
| - [Discussion of Biases](#discussion-of-biases) |
| - [Other Known Limitations](#other-known-limitations) |
| - [Additional Information](#additional-information) |
| - [Dataset Curators](#dataset-curators) |
| - [Licensing Information](#licensing-information) |
| - [Citation Information](#citation-information) |
| - [Contributions](#contributions) |
|
|
| ## Dataset Description |
|
|
| - **Homepage:** [Microsoft Research Sequential Question Answering (SQA) Dataset](https://msropendata.com/datasets/b25190ed-0f59-47b1-9211-5962858142c2) |
| - **Repository:** |
| - **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) |
| - **Leaderboard:** |
| - **Point of Contact:** |
| - Scott Wen-tau Yih scottyih@microsoft.com |
| - Mohit Iyyer m.iyyer@gmail.com |
| - Ming-Wei Chang minchang@microsoft.com |
|
|
| ### 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/](http://www-nlp.stanford.edu/software/sempre/wikitable/) |
| |
| ### 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](https://msropendata-web-api.azurewebsites.net/licenses/2f933be3-284d-500b-7ea3-2aa2fd0f1bb2/view). |
| |
| ### 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](https://github.com/mattbui) for adding this dataset. |
