Datasets:
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
languages:
- en
licenses:
- cc-by-3-0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
Dataset Card for WikiMovies
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: WikiMovies Homepage
- Repository:
- Paper: Key-Value Memory Networks for Directly Reading Documents
- Leaderboard:
- Point of Contact:
Dataset Summary
The WikiMovies dataset consists of roughly 100k (templated) questions over 75k entitiesbased on questions with answers in the open movie database (OMDb). It is the QA part of the Movie Dialog dataset.
Supported Tasks and Leaderboards
- Question Answering
Languages
The text in the dataset is written in English.
Dataset Structure
Data Instances
The raw data consists of question answer pairs separated by a tab. Here are 3 examples:
1 what does Grégoire Colin appear in? Before the Rain
1 Joe Thomas appears in which movies? The Inbetweeners Movie, The Inbetweeners 2
1 what films did Michelle Trachtenberg star in? Inspector Gadget, Black Christmas, Ice Princess, Harriet the Spy, The Scribbler
It is unclear what the 1 is for at the beginning of each line, but it has been removed in the Dataset object.
Data Fields
Here is an example of the raw data ingested by Datasets:
{
'answer': 'Before the Rain',
'question': 'what does Grégoire Colin appear in?'
}
answer: a string containing the answer to a corresponding question.
question: a string containing the relevant question.
Data Splits
The data is split into train, test, and dev sets. The split sizes are as follows:
| wiki-entities_qa_* | n examples |
|---|---|
| train.txt | 96185 |
| dev.txt | 10000 |
| test.txt | 9952 |
Dataset Creation
Curation Rationale
WikiMovies was built with the following goals in mind: (i) machine learning techniques should have ample training examples for learning; and (ii) one can analyze easily the performance of different representations of knowledge and break down the results by question type. The datasetcan be downloaded fromhttp://fb.ai/babi
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]