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Browse files- wmt20_mlqe_task3.py +0 -280
wmt20_mlqe_task3.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""WMT MLQE Shared task 3."""
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import csv
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import os
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import datasets
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_CITATION = """
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Not available.
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"""
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_DESCRIPTION = """\
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This shared task (part of WMT20) will build on its previous editions
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to further examine automatic methods for estimating the quality
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of neural machine translation output at run-time, without relying
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on reference translations. As in previous years, we cover estimation
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at various levels. Important elements introduced this year include: a new
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task where sentences are annotated with Direct Assessment (DA)
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scores instead of labels based on post-editing; a new multilingual
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sentence-level dataset mainly from Wikipedia articles, where the
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source articles can be retrieved for document-wide context; the
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availability of NMT models to explore system-internal information for the task.
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The goal of this task 3 is to predict document-level quality scores as well as fine-grained annotations.
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"""
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_HOMEPAGE = "http://www.statmt.org/wmt20/quality-estimation-task.html"
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_LICENSE = "Unknown"
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_URLs = {
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"train+dev": "https://github.com/deep-spin/deep-spin.github.io/raw/master/docs/data/wmt2020_qe/qe-task3-enfr-traindev.tar.gz",
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"test": "https://github.com/deep-spin/deep-spin.github.io/raw/master/docs/data/wmt2020_qe/qe-enfr-blindtest.tar.gz",
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}
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_ANNOTATION_CATEGORIES = [
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"Addition",
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"Agreement",
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"Ambiguous Translation",
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"Capitalization",
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"Character Encoding",
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"Company Terminology",
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"Date/Time",
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"Diacritics",
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"Duplication",
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"False Friend",
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"Grammatical Register",
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"Hyphenation",
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"Inconsistency",
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"Lexical Register",
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"Lexical Selection",
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"Named Entity",
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"Number",
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"Omitted Auxiliary Verb",
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"Omitted Conjunction",
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"Omitted Determiner",
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"Omitted Preposition",
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"Omitted Pronoun",
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"Orthography",
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"Other POS Omitted",
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"Over-translation",
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"Overly Literal",
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"POS",
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"Punctuation",
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"Shouldn't Have Been Translated",
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"Shouldn't have been translated",
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"Spelling",
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"Tense/Mood/Aspect",
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"Under-translation",
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"Unidiomatic",
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"Unintelligible",
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"Unit Conversion",
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"Untranslated",
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"Whitespace",
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"Word Order",
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"Wrong Auxiliary Verb",
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"Wrong Conjunction",
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"Wrong Determiner",
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"Wrong Language Variety",
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"Wrong Preposition",
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"Wrong Pronoun",
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]
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class Wmt20MlqeTask3(datasets.GeneratorBasedBuilder):
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"""WMT MLQE Shared task 3."""
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name="plain_text",
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version=datasets.Version("1.1.0"),
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description="Plain text",
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)
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]
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def _info(self):
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features = datasets.Features(
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{
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"document_id": datasets.Value("string"),
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"source_segments": datasets.Sequence(datasets.Value("string")),
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"source_tokenized": datasets.Sequence(datasets.Value("string")),
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"mt_segments": datasets.Sequence(datasets.Value("string")),
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"mt_tokenized": datasets.Sequence(datasets.Value("string")),
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"annotations": datasets.Sequence(
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{
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"segment_id": datasets.Sequence(datasets.Value("int32")),
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"annotation_start": datasets.Sequence(datasets.Value("int32")),
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"annotation_length": datasets.Sequence(datasets.Value("int32")),
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"severity": datasets.ClassLabel(names=["minor", "major", "critical"]),
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"severity_weight": datasets.Value("float32"),
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"category": datasets.ClassLabel(names=_ANNOTATION_CATEGORIES),
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}
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),
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"token_annotations": datasets.Sequence(
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{
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"segment_id": datasets.Sequence(datasets.Value("int32")),
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"first_token": datasets.Sequence(datasets.Value("int32")),
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"last_token": datasets.Sequence(datasets.Value("int32")),
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"token_after_gap": datasets.Sequence(datasets.Value("int32")),
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"severity": datasets.ClassLabel(names=["minor", "major", "critical"]),
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"category": datasets.ClassLabel(names=_ANNOTATION_CATEGORIES),
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}
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),
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"token_index": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("int32")))),
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"total_words": datasets.Value("int32"),
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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supervised_keys=None,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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downloaded_files = dl_manager.download(_URLs)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"main_dir": "task3/train",
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"split": "train",
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"files": dl_manager.iter_archive(downloaded_files["train+dev"]),
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"main_dir": "test-blind",
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"split": "test",
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"files": dl_manager.iter_archive(downloaded_files["test"]),
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"main_dir": "task3/dev",
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"split": "dev",
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"files": dl_manager.iter_archive(downloaded_files["train+dev"]),
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},
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),
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]
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def _generate_examples(self, main_dir, split, files):
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"""Yields examples."""
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prev_folder = None
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source_segments, source_tokenized, mt_segments, mt_tokenized = [None] * 4
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token_index, total_words, annotations, token_annotations = [], [], [], []
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for path, f in files:
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if path.startswith(main_dir):
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dir_name = path.split("/")[main_dir.count("/") + 1]
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folder = main_dir + "/" + dir_name
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if prev_folder is not None and prev_folder != folder:
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yield prev_folder, {
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"document_id": os.path.basename(prev_folder),
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"source_segments": source_segments,
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"source_tokenized": source_tokenized,
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"mt_segments": mt_segments,
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"mt_tokenized": mt_tokenized,
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"annotations": annotations,
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"token_annotations": token_annotations,
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"token_index": token_index,
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"total_words": total_words,
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}
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source_segments, source_tokenized, mt_segments, mt_tokenized = [None] * 4
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token_index, total_words, annotations, token_annotations = [], [], [], []
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prev_folder = folder
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source_segments_path = "/".join([folder, "source.segments"])
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source_tokenized_path = "/".join([folder, "source.tokenized"])
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mt_segments_path = "/".join([folder, "mt.segments"])
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mt_tokenized_path = "/".join([folder, "mt.tokenized"])
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total_words_path = "/".join([folder, "total_words"])
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token_index_path = "/".join([folder, "token_index"])
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if path == source_segments_path:
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source_segments = f.read().decode("utf-8").splitlines()
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elif path == source_tokenized_path:
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source_tokenized = f.read().decode("utf-8").splitlines()
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elif path == mt_segments_path:
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mt_segments = f.read().decode("utf-8").splitlines()
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elif path == mt_tokenized_path:
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mt_tokenized = f.read().decode("utf-8").splitlines()
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elif path == total_words_path:
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total_words = f.read().decode("utf-8").splitlines()[0]
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elif path == token_index_path:
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token_index = [
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[idx.split(" ") for idx in line.split("\t")]
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for line in f.read().decode("utf-8").splitlines()
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if line != ""
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]
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if split in ["train", "dev"]:
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annotations_path = "/".join([folder, "annotations.tsv"])
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token_annotations_path = "/".join([folder, "token_annotations.tsv"])
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if path == annotations_path:
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lines = (line.decode("utf-8") for line in f)
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reader = csv.DictReader(lines, delimiter="\t")
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annotations = [
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{
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"segment_id": row["segment_id"].split(" "),
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"annotation_start": row["annotation_start"].split(" "),
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"annotation_length": row["annotation_length"].split(" "),
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"severity": row["severity"],
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"severity_weight": row["severity_weight"],
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"category": row["category"],
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}
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for row in reader
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]
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elif path == token_annotations_path:
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lines = (line.decode("utf-8") for line in f)
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reader = csv.DictReader(lines, delimiter="\t")
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token_annotations = [
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{
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"segment_id": row["segment_id"].split(" "),
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"first_token": row["first_token"].replace("-", "-1").split(" "),
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"last_token": row["last_token"].replace("-", "-1").split(" "),
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"token_after_gap": row["token_after_gap"].replace("-", "-1").split(" "),
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"severity": row["severity"],
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"category": row["category"],
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}
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for row in reader
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]
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if prev_folder is not None:
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yield prev_folder, {
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"document_id": os.path.basename(prev_folder),
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"source_segments": source_segments,
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"source_tokenized": source_tokenized,
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"mt_segments": mt_segments,
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"mt_tokenized": mt_tokenized,
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"annotations": annotations,
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"token_annotations": token_annotations,
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"token_index": token_index,
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"total_words": total_words,
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}
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