Datasets:
Tasks:
Translation
Multilinguality:
translation
Size Categories:
1K<n<10K
Language Creators:
found
Source Datasets:
extended|amazon_us_reviews
Tags:
License:
| # 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, | |
| } | |