Datasets:
Tasks:
Translation
Size Categories:
1K<n<10K
Language Creators:
crowdsourced
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 8,110 Bytes
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
import json
import os
import re
import datasets
_CITATION = """\
@inproceedings{cmu_dog_emnlp18,
title={A Dataset for Document Grounded Conversations},
author={Zhou, Kangyan and Prabhumoye, Shrimai and Black, Alan W},
year={2018},
booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}
}
@inproceedings{khanuja-etal-2020-gluecos,
title = "{GLUEC}o{S}: An Evaluation Benchmark for Code-Switched {NLP}",
author = "Khanuja, Simran and
Dandapat, Sandipan and
Srinivasan, Anirudh and
Sitaram, Sunayana and
Choudhury, Monojit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.329",
pages = "3575--3585"
}
"""
_DESCRIPTION = """\
This is a collection of text conversations in Hinglish (code mixing between Hindi-English) and their corresponding English only versions. Can be used for Translating between the two.
"""
_HOMEPAGE = "http://festvox.org/cedar/data/notyet/"
_URL_HINGLISH = "http://festvox.org/cedar/data/notyet/CMUHinglishDoG.zip"
# From: https://github.com/festvox/datasets-CMU_DoG/archive/master/Conversations.zip
_URL_ENGLISH = "data-english.zip"
class CMUHinglishDoG(datasets.GeneratorBasedBuilder):
"""Load the CMU Hinglish DoG Data for MT"""
def _info(self):
features = datasets.Features(
{
"date": datasets.Value("string"),
"docIdx": datasets.Value("int64"),
"translation": datasets.Translation(languages=["en", "hi_en"]),
"uid": datasets.Value("string"),
"utcTimestamp": datasets.Value("string"),
"rating": datasets.Value("int64"),
"status": datasets.Value("int64"),
"uid1LogInTime": datasets.Value("string"),
"uid1LogOutTime": datasets.Value("string"),
"uid1response": {
"response": datasets.Sequence(datasets.Value("int64")),
"type": datasets.Value("string"),
},
"uid2response": {
"response": datasets.Sequence(datasets.Value("int64")),
"type": datasets.Value("string"),
},
"user2_id": datasets.Value("string"),
"whoSawDoc": datasets.Sequence(datasets.Value("string")),
"wikiDocumentIdx": datasets.Value("int64"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""The linking part between Hinglish data and English data is inspired from the implementation in GLUECoS.
Refer here for the original script https://github.com/microsoft/GLUECoS/blob/7fdc51653e37a32aee17505c47b7d1da364fa77e/Data/Preprocess_Scripts/preprocess_mt_en_hi.py"""
eng_path = dl_manager.download_and_extract(_URL_ENGLISH)
data_dir_en = os.path.join(eng_path, "Conversations")
hi_en_path = dl_manager.download_and_extract(_URL_HINGLISH)
data_dir_hi_en = os.path.join(hi_en_path, "CMUHinglishDoG", "Conversations_Hinglish")
hi_en_dirs = {
"train": os.path.join(data_dir_hi_en, "train"),
"valid": os.path.join(data_dir_hi_en, "valid"),
"test": os.path.join(data_dir_hi_en, "test"),
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"hi_en_dir": hi_en_dirs["train"],
"data_dir_en": data_dir_en,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"hi_en_dir": hi_en_dirs["test"],
"data_dir_en": data_dir_en,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"hi_en_dir": hi_en_dirs["valid"],
"data_dir_en": data_dir_en,
},
),
]
def _generate_examples(self, hi_en_dir, data_dir_en):
"""Yields examples."""
english_files_train = os.listdir(os.path.join(data_dir_en, "train"))
english_files_val = os.listdir(os.path.join(data_dir_en, "valid"))
english_files_test = os.listdir(os.path.join(data_dir_en, "test"))
hinglish_files = os.listdir(hi_en_dir)
key = 0
for f in hinglish_files:
en_file_path = f.split(".json")[0] + ".json"
found = True
# Looks for the corresponding english file in all 3 splits
if en_file_path in english_files_train:
en = json.load(open(os.path.join(os.path.join(data_dir_en, "train"), en_file_path)))
elif en_file_path in english_files_val:
en = json.load(open(os.path.join(os.path.join(data_dir_en, "valid"), en_file_path)))
elif en_file_path in english_files_test:
en = json.load(open(os.path.join(os.path.join(data_dir_en, "test"), en_file_path)))
else:
found = False
if found:
hi_en = json.load(open(os.path.join(hi_en_dir, f)))
assert len(en["history"]) == len(hi_en["history"])
for x, y in zip(en["history"], hi_en["history"]):
assert x["docIdx"] == y["docIdx"]
assert x["uid"] == y["uid"]
assert x["utcTimestamp"] == y["utcTimestamp"]
x["text"] = re.sub("\t|\n", " ", x["text"])
y["text"] = re.sub("\t|\n", " ", y["text"])
line = {
"date": hi_en["date"],
"uid": x["uid"],
"docIdx": x["docIdx"],
"utcTimestamp": x["utcTimestamp"],
"translation": {"hi_en": y["text"], "en": x["text"]},
"rating": hi_en["rating"],
"status": hi_en["status"],
"uid1LogOutTime": hi_en.get("uid1LogOutTime"),
"uid1LogInTime": hi_en["uid1LogInTime"],
"uid1response": {
"response": hi_en["uid1response"]["response"] if "uid1response" in hi_en else [],
"type": hi_en["uid1response"]["type"] if "uid1response" in hi_en else None,
},
"uid2response": {
"response": hi_en["uid2response"]["response"] if "uid2response" in hi_en else [],
"type": hi_en["uid2response"]["type"] if "uid2response" in hi_en else None,
},
"user2_id": hi_en["user2_id"],
"whoSawDoc": hi_en["whoSawDoc"],
"wikiDocumentIdx": hi_en["wikiDocumentIdx"],
}
yield key, line
key += 1
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