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# 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,
}
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