from pathlib import Path from typing import List import datasets from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses, Tasks _DATASETNAME = "sap_wat" _LANGUAGES = ["eng", "ind", "zlm", "tha", "vie"] _CITATION = """\ @inproceedings{buschbeck-exel-2020-parallel, title = "A Parallel Evaluation Data Set of Software Documentation with Document Structure Annotation", author = "Buschbeck, Bianka and Exel, Miriam", editor = "Nakazawa, Toshiaki and Nakayama, Hideki and Ding, Chenchen and Dabre, Raj and Kunchukuttan, Anoop and Pa, Win Pa and Bojar, Ond{\v{r}}ej and Parida, Shantipriya and Goto, Isao and Mino, Hidaya and Manabe, Hiroshi and Sudoh, Katsuhito and Kurohashi, Sadao and Bhattacharyya, Pushpak", booktitle = "Proceedings of the 7th Workshop on Asian Translation", month = dec, year = "2020", address = "Suzhou, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wat-1.20", pages = "160--169", abstract = "This paper accompanies the software documentation data set for machine translation, a parallel evaluation data set of data originating from the SAP Help Portal, that we released to the machine translation community for research purposes. It offers the possibility to tune and evaluate machine translation systems in the domain of corporate software documentation and contributes to the availability of a wider range of evaluation scenarios. The data set comprises of the language pairs English to Hindi, Indonesian, Malay and Thai, and thus also increases the test coverage for the many low-resource language pairs. Unlike most evaluation data sets that consist of plain parallel text, the segments in this data set come with additional metadata that describes structural information of the document context. We provide insights into the origin and creation, the particularities and characteristics of the data set as well as machine translation results.", } """ _DESCRIPTION = """The data set originates from the SAP Help Portal that contains documentation for SAP products and user assistance for product-related questions. The data has been processed in a way that makes it suitable as development and test data for machine translation purposes. The current language scope is English to Hindi, Indonesian, Japanese, Korean, Malay, Thai, Vietnamese, Simplified Chinese and Traditional Chinese. For each language pair about 4k segments are available, split into development and test data. The segments are provided in their document context and are annotated with additional metadata from the document.""" _HOMEPAGE = "https://github.com/SAP/software-documentation-data-set-for-machine-translation" _LICENSE = Licenses.CC_BY_NC_4_0.value _URLs = { _DATASETNAME: "https://raw.githubusercontent.com/SAP/software-documentation-data-set-for-machine-translation/master/{split}_data/en{lang}/software_documentation.{split}.en{lang}.{appx}" } _SUPPORTED_TASKS = [ Tasks.MACHINE_TRANSLATION ] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" _SUBSET = ["id", "ms", "th", "vi"] _LOCAL = False class SapWatDataset(datasets.GeneratorBasedBuilder): """SAP WAT is a software documentation dataset for machine translation. The current language scope is English to Hindi, Indonesian, Japanese, Korean, Malay, Thai, Vietnamese, Simplified Chinese and Traditional Chinese. Here, we only consider EN-ID, EN-TH, EN-MS, EN-VI""" BUILDER_CONFIGS = [ SEACrowdConfig( name=f"{_DATASETNAME}_en_{lang}_source", version=datasets.Version(_SOURCE_VERSION), description=f"SAP WAT source schema for EN-{lang.upper()}", schema="source", subset_id=f"{_DATASETNAME}_en_{lang}", ) for lang in _SUBSET] + [ SEACrowdConfig( name=f"{_DATASETNAME}_en_{lang}_seacrowd_t2t", version=datasets.Version(_SEACROWD_VERSION), description=f"SAP WAT SEACrowd schema for EN-{lang.upper()}", schema="seacrowd_t2t", subset_id=f"{_DATASETNAME}_en_{lang}", ) for lang in _SUBSET ] DEFAULT_CONFIG_NAME = "sap_wat_en_id_source" def _info(self): if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "text": datasets.Value("string"), "label": datasets.Value("string") } ) elif self.config.schema == "seacrowd_t2t": features = schemas.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]: lang = self.config.name.split("_")[3] splits = {datasets.Split.VALIDATION: "dev", datasets.Split.TEST: "test"} data_urls = { split: _URLs[_DATASETNAME].format(split=splits[split], lang=lang, appx=lang) for split in splits } dl_paths = dl_manager.download(data_urls) en_data_urls = { split: _URLs[_DATASETNAME].format(split=splits[split], lang=lang, appx="en") for split in splits } en_dl_paths = dl_manager.download(en_data_urls) return [ datasets.SplitGenerator( name=split, gen_kwargs={"filepath": dl_paths[split], "en_filepath": en_dl_paths[split]}, ) for split in splits ] def _generate_examples(self, filepath: Path, en_filepath: Path): with open(en_filepath, "r") as f: lines_1 = f.readlines() with open(filepath, "r") as f: lines_2 = f.readlines() if self.config.schema == "source": for _id, (line_1, line_2) in enumerate(zip(lines_1, lines_2)): ex = { "id": _id, "text": line_1.strip(), "label": line_2.strip() } yield _id, ex elif self.config.schema == "seacrowd_t2t": lang = self.config.name.split("_")[3] lang_name = _LANGUAGES[_SUBSET.index(lang)+1] for _id, (line_1, line_2) in enumerate(zip(lines_1, lines_2)): ex = { "id": _id, "text_1": line_1.strip(), "text_2": line_2.strip(), "text_1_name": 'eng', "text_2_name": lang_name, } yield _id, ex else: raise ValueError(f"Invalid config: {self.config.name}")