# 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" _URL_ENGLISH = "https://github.com/festvox/datasets-CMU_DoG/archive/master/Conversations.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, "datasets-CMU_DoG-master", "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