from dataclasses import dataclass import datasets from datasets.info import DatasetInfo from datasets.utils.download_manager import DownloadManager import os _DESCRIPTION = """A system's task on any of the XL-WiC datasets is to identify the intended meaning of a word in a context of a given language. XL-WiC is framed as a binary classification task. Each instance in XL-WiC has a target word w, either a verb or a noun, for which two contexts are provided. Each of these contexts triggers a specific meaning of w. The task is to identify if the occurrences of w in the two contexts correspond to the same meaning or not. XL-WiC provides dev and test sets in the following 12 languages: Bulgarian (BG) Danish (DA) German (DE) Estonian (ET) Farsi (FA) French (FR) Croatian (HR) Italian (IT) Japanese (JA) Korean (KO) Dutch (NL) Chinese (ZH) and training sets in the following 3 languages: German (DE) French (FR) Italian (IT) """ _CITATION = """@inproceedings{raganato-etal-2020-xl-wic, title={XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization}, author={Raganato, Alessandro and Pasini, Tommaso and Camacho-Collados, Jose and Pilehvar, Mohammad Taher}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, pages={7193--7206}, year={2020} } """ _DOWNLOAD_URL = "https://pilehvar.github.io/xlwic/data/xlwic_datasets.zip" _VERSION = "1.0.0" _WN_LANGS = ["EN", "BG", "ZH", "HR", "DA", "NL", "ET", "FA", "JA", "KO"] _WIKT_LANGS = ["IT", "FR", "DE"] _CODE_TO_LANG_ID = { "EN": "english", "BG": "bulgarian", "ZH": "chinese", "HR": "croatian", "DA": "danish", "NL": "dutch", "ET": "estonian", "FA": "farsi", "JA": "japanese", "KO": "korean", "IT": "italian", "FR": "french", "DE": "german", } _AVAILABLE_PAIRS = ( list(zip(["EN"] * (len(_WN_LANGS) - 1), _WN_LANGS[1:])) + list(zip(["EN"] * len(_WIKT_LANGS), _WIKT_LANGS)) + [("IT", "IT"), ("FR", "FR"), ("DE", "DE")] ) @dataclass class XLWiCConfig(datasets.BuilderConfig): version:str=None training_lang:str = None target_lang:str = None name:str = None class XLWIC(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ XLWiCConfig( name=f"xlwic_{source.lower()}_{target.lower()}", training_lang=source, target_lang=target, version=datasets.Version(_VERSION, ""), ) for source, target in _AVAILABLE_PAIRS ] def _info(self) -> DatasetInfo: return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "id": datasets.Value("string"), "context_1": datasets.Value("string"), "context_2": datasets.Value("string"), "target_word": datasets.Value("string"), "pos": datasets.Value("string"), "target_word_location_1": { "char_start": datasets.Value("int32"), "char_end": datasets.Value("int32"), }, "target_word_location_2": { "char_start": datasets.Value("int32"), "char_end": datasets.Value("int32"), }, "language": datasets.Value("string"), "label": datasets.Value("int32"), } ), supervised_keys=None, homepage="https://pilehvar.github.io/xlwic/", citation=_CITATION, ) def _split_generators(self, dl_manager: DownloadManager): downloaded_file = dl_manager.download_and_extract(_DOWNLOAD_URL) dataset_root_folder = os.path.join(downloaded_file, "xlwic_datasets") return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "dataset_root": dataset_root_folder, "lang": self.config.training_lang, "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "dataset_root": dataset_root_folder, "lang": self.config.target_lang, "split": "valid", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "dataset_root": dataset_root_folder, "lang": self.config.target_lang, "split": "test", }, ), ] def _yield_from_lines(self, lines, lang): for i, ( tw, pos, char_start_1, char_end_1, char_start_2, char_end_2, context_1, context_2, label, ) in enumerate(lines): _id = f"{lang}_{i}" yield _id, { "id": _id, "target_word": tw, "context_1": context_1, "context_2": context_2, "label": int(label), "target_word_location_1": { "char_start": int(char_start_1), "char_end": int(char_end_1), }, "target_word_location_2": { "char_start": int(char_start_2), "char_end": int(char_end_2) }, "pos": pos, "language": lang, } def _from_selfcontained_file(self, dataset_root, lang, split): ext_lang = _CODE_TO_LANG_ID[lang] if lang in _WIKT_LANGS: path = os.path.join( dataset_root, "xlwic_wikt", f"{ext_lang}_{lang.lower()}", f"{lang.lower()}_{split}.txt", ) elif lang != "EN" and lang in _WN_LANGS: path = os.path.join( dataset_root, "xlwic_wn", f"{ext_lang}_{lang.lower()}", f"{lang.lower()}_{split}.txt", ) elif lang == "EN" and lang in _WN_LANGS: path = os.path.join( dataset_root, "wic_english", f"{split}_{lang.lower()}.txt" ) with open(path) as lines: all_lines = [line.strip().split("\t") for line in lines] yield from self._yield_from_lines(all_lines, lang) def _from_test_files(self, dataset_root, lang, split): ext_lang = _CODE_TO_LANG_ID[lang] if lang in _WIKT_LANGS: path_data = os.path.join( dataset_root, "xlwic_wikt", f"{ext_lang}_{lang.lower()}", f"{lang.lower()}_{split}_data.txt", ) elif lang != "EN" and lang in _WN_LANGS: path_data = os.path.join( dataset_root, "xlwic_wn", f"{ext_lang}_{lang.lower()}", f"{lang.lower()}_{split}_data.txt", ) path_gold = path_data.replace('_data.txt', '_gold.txt') with open(path_data) as lines: all_lines = [line.strip().split("\t") for line in lines] with open(path_gold) as lines: all_labels = [line.strip() for line in lines] for line, label in zip(all_lines, all_labels): line.append(label) yield from self._yield_from_lines(all_lines, lang) def _generate_examples(self, dataset_root, lang, split, **kwargs): if split in {"train", "valid"}: yield from self._from_selfcontained_file(dataset_root, lang, split) else: yield from self._from_test_files(dataset_root, lang, split)