annotations_creators:
- expert-generated
- found
- no-annotation
language_creators:
- found
language:
- chr
- en
license:
- other
multilinguality:
- monolingual
- multilingual
- translation
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- fill-mask
- text-generation
- translation
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: chren
config_names:
- monolingual
- monolingual_raw
- parallel
- parallel_raw
dataset_info:
- config_name: monolingual
features:
- name: sentence
dtype: string
splits:
- name: chr
num_bytes: 882824
num_examples: 5210
- name: en5000
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num_examples: 5000
- name: en10000
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num_examples: 10000
- name: en20000
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num_examples: 20000
- name: en50000
num_bytes: 6065580
num_examples: 49999
- name: en100000
num_bytes: 12130164
num_examples: 100000
download_size: 16967664
dataset_size: 23337746
- config_name: monolingual_raw
features:
- name: text_sentence
dtype: string
- name: text_title
dtype: string
- name: speaker
dtype: string
- name: date
dtype: int32
- name: type
dtype: string
- name: dialect
dtype: string
splits:
- name: full
num_bytes: 1210056
num_examples: 5210
download_size: 410646
dataset_size: 1210056
- config_name: parallel
features:
- name: sentence_pair
dtype:
translation:
languages:
- en
- chr
splits:
- name: train
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num_examples: 11639
- name: dev
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num_examples: 1000
- name: out_dev
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num_examples: 256
- name: test
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num_examples: 1000
- name: out_test
num_bytes: 80959
num_examples: 256
download_size: 2143266
dataset_size: 3773643
- config_name: parallel_raw
features:
- name: line_number
dtype: string
- name: sentence_pair
dtype:
translation:
languages:
- en
- chr
- name: text_title
dtype: string
- name: speaker
dtype: string
- name: date
dtype: int32
- name: type
dtype: string
- name: dialect
dtype: string
splits:
- name: full
num_bytes: 5010734
num_examples: 14151
download_size: 2018726
dataset_size: 5010734
configs:
- config_name: monolingual
data_files:
- split: chr
path: monolingual/chr-*
- split: en5000
path: monolingual/en5000-*
- split: en10000
path: monolingual/en10000-*
- split: en20000
path: monolingual/en20000-*
- split: en50000
path: monolingual/en50000-*
- split: en100000
path: monolingual/en100000-*
- config_name: monolingual_raw
data_files:
- split: full
path: monolingual_raw/full-*
- config_name: parallel
data_files:
- split: train
path: parallel/train-*
- split: dev
path: parallel/dev-*
- split: out_dev
path: parallel/out_dev-*
- split: test
path: parallel/test-*
- split: out_test
path: parallel/out_test-*
default: true
- config_name: parallel_raw
data_files:
- split: full
path: parallel_raw/full-*
Dataset Card for ChrEn
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Repository: Github repository for ChrEn
- Paper: ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization
- Point of Contact: benfrey@email.unc.edu
Dataset Summary
ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English. ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation. ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning.
Supported Tasks and Leaderboards
The dataset is intended to use for machine-translation between Enlish (en) and Cherokee (chr).
Languages
The dataset contains Enlish (en) and Cherokee (chr) text. The data encompasses both existing dialects of Cherokee: the Overhill dialect, mostly spoken in Oklahoma (OK), and the Middle dialect, mostly used in North Carolina (NC).
Dataset Structure
Data Instances
[More Information Needed]
Data Fields
[More Information Needed]
Data Splits
[More Information Needed]
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
Many of the source texts were translations of English materials, which means that the Cherokee structures may not be 100% natural in terms of what a speaker might spontaneously produce. Each text was translated by people who speak Cherokee as the first language, which means there is a high probability of grammaticality. These data were originally available in PDF version. We apply the Optical Character Recognition (OCR) via Tesseract OCR engine to extract the Cherokee and English text.
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
The sentences were manually aligned by Dr. Benjamin Frey a proficient second-language speaker of Cherokee, who also fixed the errors introduced by OCR. This process is time-consuming and took several months.
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
The dataset was gathered and annotated by Shiyue Zhang, Benjamin Frey, and Mohit Bansal at UNC Chapel Hill.
Licensing Information
The copyright of the data belongs to original book/article authors or translators (hence, used for research purpose; and please contact Dr. Benjamin Frey for other copyright questions).
Citation Information
@inproceedings{zhang2020chren,
title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization},
author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit},
booktitle={EMNLP2020},
year={2020}
}