Datasets:
Tasks:
Translation
Multilinguality:
translation
Size Categories:
1M<n<10M
Language Creators:
expert-generated
Annotations Creators:
crowdsourced
Source Datasets:
extended|ted_talks_iwslt
Tags:
License:
# coding=utf-8 | |
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
# Lint as: python3 | |
"""TED talk high/low-resource paired language data set from Qi, et al. 2018.""" | |
import datasets | |
_DESCRIPTION = """\ | |
Data sets derived from TED talk transcripts for comparing similar language pairs | |
where one is high resource and the other is low resource. | |
""" | |
_CITATION = """\ | |
@inproceedings{Ye2018WordEmbeddings, | |
author = {Ye, Qi and Devendra, Sachan and Matthieu, Felix and Sarguna, Padmanabhan and Graham, Neubig}, | |
title = {When and Why are pre-trained word embeddings useful for Neural Machine Translation}, | |
booktitle = {HLT-NAACL}, | |
year = {2018}, | |
} | |
""" | |
_DATA_URL = "http://www.phontron.com/data/qi18naacl-dataset.tar.gz" | |
_VALID_LANGUAGE_PAIRS = ( | |
("az", "en"), | |
("az_tr", "en"), | |
("be", "en"), | |
("be_ru", "en"), | |
("es", "pt"), | |
("fr", "pt"), | |
("gl", "en"), | |
("gl_pt", "en"), | |
("he", "pt"), | |
("it", "pt"), | |
("pt", "en"), | |
("ru", "en"), | |
("ru", "pt"), | |
("tr", "en"), | |
) | |
class TedHrlrConfig(datasets.BuilderConfig): | |
"""BuilderConfig for TED talk data comparing high/low resource languages.""" | |
def __init__(self, language_pair=(None, None), **kwargs): | |
"""BuilderConfig for TED talk data comparing high/low resource languages. | |
The first language in `language_pair` should either be a 2-letter coded | |
string or two such strings joined by an underscore (e.g., "az" or "az_tr"). | |
In cases where it contains two languages, the train data set will contain an | |
(unlabelled) mix of the two languages and the validation and test sets | |
will contain only the first language. This dataset will refer to the | |
source language by the 5-letter string with the underscore. The second | |
language in `language_pair` must be a 2-letter coded string. | |
For example, to get pairings between Russian and English, specify | |
`("ru", "en")` as `language_pair`. To get a mix of Belarusian and Russian in | |
the training set and purely Belarusian in the validation and test sets, | |
specify `("be_ru", "en")`. | |
Args: | |
language_pair: pair of languages that will be used for translation. The | |
first will be used as source and second as target in supervised mode. | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
name = "%s_to_%s" % (language_pair[0].replace("_", ""), language_pair[1]) | |
description = ("Translation dataset from %s to %s in plain text.") % (language_pair[0], language_pair[1]) | |
super(TedHrlrConfig, self).__init__(name=name, description=description, **kwargs) | |
# Validate language pair. | |
assert language_pair in _VALID_LANGUAGE_PAIRS, ( | |
"Config language pair (%s, " "%s) not supported" | |
) % language_pair | |
self.language_pair = language_pair | |
class TedHrlr(datasets.GeneratorBasedBuilder): | |
"""TED talk data set for comparing high and low resource languages.""" | |
BUILDER_CONFIGS = [ | |
TedHrlrConfig( # pylint: disable=g-complex-comprehension | |
language_pair=pair, | |
version=datasets.Version("1.0.0", ""), | |
) | |
for pair in _VALID_LANGUAGE_PAIRS | |
] | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{"translation": datasets.features.Translation(languages=self.config.language_pair)} | |
), | |
homepage="https://github.com/neulab/word-embeddings-for-nmt", | |
supervised_keys=self.config.language_pair, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
archive = dl_manager.download(_DATA_URL) | |
source, target = self.config.language_pair | |
data_dir = "datasets/%s_to_%s" % (source, target) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"source_file": data_dir + "/" + f"{source.replace('_', '-')}.train", | |
"target_file": data_dir + "/" + f"{target}.train", | |
"files": dl_manager.iter_archive(archive), | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"source_file": data_dir + "/" + f"{source.split('_')[0]}.dev", | |
"target_file": data_dir + "/" + f"{target}.dev", | |
"files": dl_manager.iter_archive(archive), | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"source_file": data_dir + "/" + f"{source.split('_')[0]}.test", | |
"target_file": data_dir + "/" + f"{target}.test", | |
"files": dl_manager.iter_archive(archive), | |
}, | |
), | |
] | |
def _generate_examples(self, source_file, target_file, files): | |
"""This function returns the examples in the raw (text) form.""" | |
source_sentences, target_sentences = None, None | |
for path, f in files: | |
if path == source_file: | |
source_sentences = f.read().decode("utf-8").split("\n") | |
elif path == target_file: | |
target_sentences = f.read().decode("utf-8").split("\n") | |
if source_sentences is not None and target_sentences is not None: | |
break | |
assert len(target_sentences) == len(source_sentences), "Sizes do not match: %d vs %d for %s vs %s." % ( | |
len(source_sentences), | |
len(target_sentences), | |
source_file, | |
target_file, | |
) | |
source, target = self.config.language_pair | |
for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)): | |
result = {"translation": {source: l1, target: l2}} | |
# Make sure that both translations are non-empty. | |
if all(result.values()): | |
yield idx, result | |