# coding=utf-8 # Copyright 2022 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. from pathlib import Path from typing import Dict, List, Tuple import datasets from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import (SCHEMA_TO_FEATURES, TASK_TO_SCHEMA, Licenses, Tasks) _CITATION = """\ @inproceedings{mayhew-etal-2020-simultaneous, title = "Simultaneous Translation and Paraphrase for Language Education", author = "Mayhew, Stephen and Bicknell, Klinton and Brust, Chris and McDowell, Bill and Monroe, Will and Settles, Burr", editor = "Birch, Alexandra and Finch, Andrew and Hayashi, Hiroaki and Heafield, Kenneth and Junczys-Dowmunt, Marcin and Konstas, Ioannis and Li, Xian and Neubig, Graham and Oda, Yusuke", booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.ngt-1.28", doi = "10.18653/v1/2020.ngt-1.28", pages = "232--243", } """ _DATASETNAME = "duolingo_staple_2020" _DESCRIPTION = """\ This dataset is provided by Duolingo for their Simultaneous Translation and Paraphrase for Language Education (STAPLE) shared task in 2020. It contains English prompts and corresponding sets of plausible translations in five other languages, including Vietnamese. Each prompt is provided with a baseline automatic reference translation from Amazon, as well as some accepted translations with corresponding user response rates used for task scoring. """ _HOMEPAGE = "https://sharedtask.duolingo.com/#data" _LANGUAGES = ["vie"] _LICENSE = Licenses.CC_BY_NC_4_0.value _LOCAL = True # needs to fill a form to download the dataset (dynamic link) _URLS = "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/38OJR6&version=6.0" # `aws_baseline` refers to reference translations from Amazon Automated MT model, # while `gold` refers to translations accepted by Duolingo learners _SUBSETS = ["aws_baseline", "gold"] _SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] _SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}" # t2t _SOURCE_VERSION = "6.0.0" _SEACROWD_VERSION = "2024.06.20" class DuolingoStaple2020Dataset(datasets.GeneratorBasedBuilder): """Dataset for the Duolingo STAPLE 2020 shared task.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) BUILDER_CONFIGS = [] for subset in _SUBSETS: BUILDER_CONFIGS += [ SEACrowdConfig( name=f"{_DATASETNAME}_{subset}_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} {subset} source schema", schema="source", subset_id=subset, ), SEACrowdConfig( name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA}", version=SEACROWD_VERSION, description=f"{_DATASETNAME} {subset} SEACrowd schema", schema=_SEACROWD_SCHEMA, subset_id=subset, ), ] DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_gold_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": if self.config.subset_id == "aws_baseline": features = datasets.Features( { "prompt_id": datasets.Value("string"), "source_text": datasets.Value("string"), "translation": datasets.Value("string"), } ) elif self.config.subset_id == "gold": features = datasets.Features( { "prompt_id": datasets.Value("string"), "source_text": datasets.Value("string"), "translations": [ { "text": datasets.Value("string"), "weight": datasets.Value("float64"), } ], } ) elif self.config.schema == _SEACROWD_SCHEMA: features = SCHEMA_TO_FEATURES[TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]] # text2text_features return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" if self.config.data_dir is None: raise ValueError("This is a local dataset. Please pass the data_dir kwarg (staple-2020 dir) to load_dataset.") else: data_dir = Path(self.config.data_dir) / "en_vi" if self.config.subset_id == "aws_baseline": filename = "aws_baseline.pred" elif self.config.subset_id == "gold": filename = "2020-02-20.gold" return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_dir / f"train.en_vi.{'2020-01-13.gold' if self.config.subset_id == 'gold' else filename}.txt", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": data_dir / f"test.en_vi.{filename}.txt", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": data_dir / f"dev.en_vi.{filename}.txt", }, ), ] def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" # aws_baseline subset if self.config.subset_id == "aws_baseline": with open(filepath, "r", encoding="utf-8") as f: entries = f.read().strip().split("\n\n") for key, entry in enumerate(entries): parts = entry.split("|") prompt_id = parts[0].strip() source_text, translation = list(map(str.strip, parts[1].split("\n"))) if self.config.schema == "source": yield key, { "prompt_id": prompt_id, "source_text": source_text, "translation": translation, } elif self.config.schema == _SEACROWD_SCHEMA: yield key, { "id": str(key), "text_1": source_text, "text_2": translation, "text_1_name": "english", "text_2_name": "translation", } # gold subset elif self.config.subset_id == "gold": with open(filepath, "r", encoding="utf-8") as f: entries = f.read().strip().split("\n\n") key = 0 for entry in entries: parts = entry.split("\n") prompt_id, source_text = list(map(str.strip, parts[0].split("|"))) if self.config.schema == "source": translations = [] for answer in parts[1:]: translation, weight = list(map(str.strip, answer.split("|"))) translations.append({"text": translation, "weight": float(weight)}) yield key, { "prompt_id": prompt_id, "source_text": source_text, "translations": translations, } key += 1 elif self.config.schema == _SEACROWD_SCHEMA: for answer in parts[1:]: translation, _ = list(map(str.strip, answer.split("|"))) yield key, { "id": str(key), "text_1": source_text, "text_2": translation, "text_1_name": "english", "text_2_name": "translation", } key += 1