# 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. """WMT MLQE Shared task 3.""" import csv import os import datasets _CITATION = """ Not available. """ _DESCRIPTION = """\ This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task. The goal of this task 3 is to predict document-level quality scores as well as fine-grained annotations. """ _HOMEPAGE = "http://www.statmt.org/wmt20/quality-estimation-task.html" _LICENSE = "Unknown" _URLs = { "train+dev": "https://github.com/deep-spin/deep-spin.github.io/raw/master/docs/data/wmt2020_qe/qe-task3-enfr-traindev.tar.gz", "test": "https://github.com/deep-spin/deep-spin.github.io/raw/master/docs/data/wmt2020_qe/qe-enfr-blindtest.tar.gz", } _ANNOTATION_CATEGORIES = [ "Addition", "Agreement", "Ambiguous Translation", "Capitalization", "Character Encoding", "Company Terminology", "Date/Time", "Diacritics", "Duplication", "False Friend", "Grammatical Register", "Hyphenation", "Inconsistency", "Lexical Register", "Lexical Selection", "Named Entity", "Number", "Omitted Auxiliary Verb", "Omitted Conjunction", "Omitted Determiner", "Omitted Preposition", "Omitted Pronoun", "Orthography", "Other POS Omitted", "Over-translation", "Overly Literal", "POS", "Punctuation", "Shouldn't Have Been Translated", "Shouldn't have been translated", "Spelling", "Tense/Mood/Aspect", "Under-translation", "Unidiomatic", "Unintelligible", "Unit Conversion", "Untranslated", "Whitespace", "Word Order", "Wrong Auxiliary Verb", "Wrong Conjunction", "Wrong Determiner", "Wrong Language Variety", "Wrong Preposition", "Wrong Pronoun", ] class Wmt20MlqeTask3(datasets.GeneratorBasedBuilder): """WMT MLQE Shared task 3.""" BUILDER_CONFIGS = [ datasets.BuilderConfig( name="plain_text", version=datasets.Version("1.1.0"), description="Plain text", ) ] def _info(self): features = datasets.Features( { "document_id": datasets.Value("string"), "source_segments": datasets.Sequence(datasets.Value("string")), "source_tokenized": datasets.Sequence(datasets.Value("string")), "mt_segments": datasets.Sequence(datasets.Value("string")), "mt_tokenized": datasets.Sequence(datasets.Value("string")), "annotations": datasets.Sequence( { "segment_id": datasets.Sequence(datasets.Value("int32")), "annotation_start": datasets.Sequence(datasets.Value("int32")), "annotation_length": datasets.Sequence(datasets.Value("int32")), "severity": datasets.ClassLabel(names=["minor", "major", "critical"]), "severity_weight": datasets.Value("float32"), "category": datasets.ClassLabel(names=_ANNOTATION_CATEGORIES), } ), "token_annotations": datasets.Sequence( { "segment_id": datasets.Sequence(datasets.Value("int32")), "first_token": datasets.Sequence(datasets.Value("int32")), "last_token": datasets.Sequence(datasets.Value("int32")), "token_after_gap": datasets.Sequence(datasets.Value("int32")), "severity": datasets.ClassLabel(names=["minor", "major", "critical"]), "category": datasets.ClassLabel(names=_ANNOTATION_CATEGORIES), } ), "token_index": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("int32")))), "total_words": datasets.Value("int32"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" downloaded_files = dl_manager.download(_URLs) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "main_dir": "task3/train", "split": "train", "files": dl_manager.iter_archive(downloaded_files["train+dev"]), }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "main_dir": "test-blind", "split": "test", "files": dl_manager.iter_archive(downloaded_files["test"]), }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "main_dir": "task3/dev", "split": "dev", "files": dl_manager.iter_archive(downloaded_files["train+dev"]), }, ), ] def _generate_examples(self, main_dir, split, files): """Yields examples.""" prev_folder = None source_segments, source_tokenized, mt_segments, mt_tokenized = [None] * 4 token_index, total_words, annotations, token_annotations = [], [], [], [] for path, f in files: if path.startswith(main_dir): dir_name = path.split("/")[main_dir.count("/") + 1] folder = main_dir + "/" + dir_name if prev_folder is not None and prev_folder != folder: yield prev_folder, { "document_id": os.path.basename(prev_folder), "source_segments": source_segments, "source_tokenized": source_tokenized, "mt_segments": mt_segments, "mt_tokenized": mt_tokenized, "annotations": annotations, "token_annotations": token_annotations, "token_index": token_index, "total_words": total_words, } source_segments, source_tokenized, mt_segments, mt_tokenized = [None] * 4 token_index, total_words, annotations, token_annotations = [], [], [], [] prev_folder = folder source_segments_path = "/".join([folder, "source.segments"]) source_tokenized_path = "/".join([folder, "source.tokenized"]) mt_segments_path = "/".join([folder, "mt.segments"]) mt_tokenized_path = "/".join([folder, "mt.tokenized"]) total_words_path = "/".join([folder, "total_words"]) token_index_path = "/".join([folder, "token_index"]) if path == source_segments_path: source_segments = f.read().decode("utf-8").splitlines() elif path == source_tokenized_path: source_tokenized = f.read().decode("utf-8").splitlines() elif path == mt_segments_path: mt_segments = f.read().decode("utf-8").splitlines() elif path == mt_tokenized_path: mt_tokenized = f.read().decode("utf-8").splitlines() elif path == total_words_path: total_words = f.read().decode("utf-8").splitlines()[0] elif path == token_index_path: token_index = [ [idx.split(" ") for idx in line.split("\t")] for line in f.read().decode("utf-8").splitlines() if line != "" ] if split in ["train", "dev"]: annotations_path = "/".join([folder, "annotations.tsv"]) token_annotations_path = "/".join([folder, "token_annotations.tsv"]) if path == annotations_path: lines = (line.decode("utf-8") for line in f) reader = csv.DictReader(lines, delimiter="\t") annotations = [ { "segment_id": row["segment_id"].split(" "), "annotation_start": row["annotation_start"].split(" "), "annotation_length": row["annotation_length"].split(" "), "severity": row["severity"], "severity_weight": row["severity_weight"], "category": row["category"], } for row in reader ] elif path == token_annotations_path: lines = (line.decode("utf-8") for line in f) reader = csv.DictReader(lines, delimiter="\t") token_annotations = [ { "segment_id": row["segment_id"].split(" "), "first_token": row["first_token"].replace("-", "-1").split(" "), "last_token": row["last_token"].replace("-", "-1").split(" "), "token_after_gap": row["token_after_gap"].replace("-", "-1").split(" "), "severity": row["severity"], "category": row["category"], } for row in reader ] if prev_folder is not None: yield prev_folder, { "document_id": os.path.basename(prev_folder), "source_segments": source_segments, "source_tokenized": source_tokenized, "mt_segments": mt_segments, "mt_tokenized": mt_tokenized, "annotations": annotations, "token_annotations": token_annotations, "token_index": token_index, "total_words": total_words, }