Datasets:
annotations_creators:
- found
language:
- en
- ru
- zh
- es
- ar
- hi
- id
- te
- sw
- eu
- my
language_creators:
- found
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
paperswithcode_id: null
pretty_name: XStoryCloze
size_categories:
- 1K<n<10K
source_datasets:
- extended|story_cloze
tags: []
task_categories:
- other
task_ids: []
dataset_info:
- config_name: en
features:
- name: story_id
dtype: string
- name: input_sentence_1
dtype: string
- name: input_sentence_2
dtype: string
- name: input_sentence_3
dtype: string
- name: input_sentence_4
dtype: string
- name: sentence_quiz1
dtype: string
- name: sentence_quiz2
dtype: string
- name: answer_right_ending
dtype: int32
splits:
- name: train
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num_examples: 360
- name: eval
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num_examples: 1511
download_size: 573176
dataset_size: 614056
- config_name: ru
features:
- name: story_id
dtype: string
- name: input_sentence_1
dtype: string
- name: input_sentence_2
dtype: string
- name: input_sentence_3
dtype: string
- name: input_sentence_4
dtype: string
- name: sentence_quiz1
dtype: string
- name: sentence_quiz2
dtype: string
- name: answer_right_ending
dtype: int32
splits:
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- config_name: zh
features:
- name: story_id
dtype: string
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dtype: string
- name: input_sentence_2
dtype: string
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dtype: string
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- config_name: es
features:
- name: story_id
dtype: string
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dtype: string
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dtype: string
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- config_name: ar
features:
- name: story_id
dtype: string
- name: input_sentence_1
dtype: string
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dtype: string
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- config_name: hi
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- config_name: te
features:
- name: story_id
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- config_name: sw
features:
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dtype: string
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dtype: string
- name: input_sentence_3
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- config_name: eu
features:
- name: story_id
dtype: string
- name: input_sentence_1
dtype: string
- name: input_sentence_2
dtype: string
- name: input_sentence_3
dtype: string
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dtype: string
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dtype: string
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- config_name: my
features:
- name: story_id
dtype: string
- name: input_sentence_1
dtype: string
- name: input_sentence_2
dtype: string
- name: input_sentence_3
dtype: string
- name: input_sentence_4
dtype: string
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dtype: string
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download_size: 1967534
dataset_size: 2008414
Dataset Card for XStoryCloze
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://cs.rochester.edu/nlp/rocstories/
- Repository: More Information Needed
- Paper: Few-shot Learning with Multilingual Generative Language Models
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 2.03 MB
- Size of the generated dataset: 2.03 MB
- Total amount of disk used: 2.05 MB
Dataset Summary
XStoryCloze consists of the professionally translated version of the English StoryCloze dataset (Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI.
Supported Tasks and Leaderboards
commonsense reasoning
Languages
en, ru, zh (Simplified), es (Latin America), ar, hi, id, te, sw, eu, my.
Dataset Structure
Data Instances
- Size of downloaded dataset files: 2.03 MB
- Size of the generated dataset: 2.03 MB
- Total amount of disk used: 2.05 MB
An example of 'train' looks as follows.
{'answer_right_ending': 1,
'input_sentence_1': 'Rick grew up in a troubled household.',
'input_sentence_2': 'He never found good support in family, and turned to gangs.',
'input_sentence_3': "It wasn't long before Rick got shot in a robbery.",
'input_sentence_4': 'The incident caused him to turn a new leaf.',
'sentence_quiz1': 'He is happy now.',
'sentence_quiz2': 'He joined a gang.',
'story_id': '138d5bfb-05cc-41e3-bf2c-fa85ebad14e2'}
Data Fields
The data fields are the same among all splits.
input_sentence_1
: The first statement in the story.input_sentence_2
: The second statement in the story.input_sentence_3
: The third statement in the story.input_sentence_4
: The forth statement in the story.sentence_quiz1
: first possible continuation of the story.sentence_quiz2
: second possible continuation of the story.answer_right_ending
: correct possible ending; either 1 or 2.story_id
: story id.
Data Splits
This dataset is intended to be used for evaluating the zero- and few-shot learning capabilities of multlingual language models. We split the data for each language into train and test (360 vs. 1510 examples, respectively). The released data files for different languages maintain a line-by-line alignment.
name | train | test |
---|---|---|
en | 360 | 1510 |
ru | 360 | 1510 |
zh | 360 | 1510 |
es | 360 | 1510 |
ar | 360 | 1510 |
hi | 360 | 1510 |
id | 360 | 1510 |
te | 360 | 1510 |
sw | 360 | 1510 |
eu | 360 | 1510 |
my | 360 | 1510 |
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
XStoryCloze is opensourced under CC BY-SA 4.0, the same license as the original English StoryCloze.
Citation Information
@article{DBLP:journals/corr/abs-2112-10668,
author = {Xi Victoria Lin and
Todor Mihaylov and
Mikel Artetxe and
Tianlu Wang and
Shuohui Chen and
Daniel Simig and
Myle Ott and
Naman Goyal and
Shruti Bhosale and
Jingfei Du and
Ramakanth Pasunuru and
Sam Shleifer and
Punit Singh Koura and
Vishrav Chaudhary and
Brian O'Horo and
Jeff Wang and
Luke Zettlemoyer and
Zornitsa Kozareva and
Mona T. Diab and
Veselin Stoyanov and
Xian Li},
title = {Few-shot Learning with Multilingual Language Models},
journal = {CoRR},
volume = {abs/2112.10668},
year = {2021},
url = {https://arxiv.org/abs/2112.10668},
eprinttype = {arXiv},
eprint = {2112.10668},
timestamp = {Tue, 04 Jan 2022 15:59:27 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2112-10668.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Contributions
Thanks to @juletx.