Datasets:

inseq
/

disc_eval_mt

	# Copyright 2023 The Inseq Team. All rights reserved.
	#
	# 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.
	"""DiscEvalMT: Contrastive test sets for the evaluation of discourse in machine translation (v2)"""

	from typing import Dict

	import datasets
	from datasets.utils.download_manager import DownloadManager

	_CITATION = """\
	@inproceedings{bawden-etal-2018-evaluating,
	title = "Evaluating Discourse Phenomena in Neural Machine Translation",
	author = "Bawden, Rachel and Sennrich, Rico and Birch, Alexandra and Haddow, Barry",
	booktitle = {{Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)}},
	month = jun,
	year = "2018",
	address = "New Orleans, Louisiana",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/N18-1118",
	doi = "10.18653/v1/N18-1118",
	pages = "1304--1313"
	}
	"""

	_DESCRIPTION = """\
	The test sets comprise hand-crafted examples that are inspired by similar examples in the parallel corpus OpenSubtitles2016 (in terms of vocabulary usage, style and syntactic formulation)
	for the evaluation of discourse in English-to-French machine translation.
	"""

	_URL = "https://huggingface.co/datasets/inseq/disc_eval_mt/raw/main/examples"

	_HOMEPAGE = "https://github.com/rbawden/discourse-mt-test-sets"

	_LICENSE = "CC-BY-SA-4.0"

	_CONFIGS = ["anaphora", "lexical-choice"]


	class DiscEvalMTConfig(datasets.BuilderConfig):
	def __init__(self, source_language: str, target_language: str, **kwargs):
	"""BuilderConfig for DiscEvalMT.
	Args:
	source_language: `str`, source language for translation.
	target_language: `str`, translation language.
	**kwargs: keyword arguments forwarded to super.
	"""
	super().__init__(**kwargs)
	self.source_language = source_language
	self.target_language = target_language


	class DiscEvalMT(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	DiscEvalMTConfig(
	name=cfg,
	source_language="en",
	target_language="fr",
	)
	for cfg in _CONFIGS
	]

	DEFAULT_CONFIG_NAME = "anaphora"

	def _info(self):
	features = datasets.Features(
	{
	"id": datasets.Value("int32"),
	"context_en": datasets.Value("string"),
	"en": datasets.Value("string"),
	"context_fr": datasets.Value("string"),
	"fr": datasets.Value("string"),
	"contrast_fr": datasets.Value("string"),
	"context_en_with_tags": datasets.Value("string"),
	"en_with_tags": datasets.Value("string"),
	"context_fr_with_tags": datasets.Value("string"),
	"fr_with_tags": datasets.Value("string"),
	"contrast_fr_with_tags": datasets.Value("string"),
	"type": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	@staticmethod
	def clean_string(txt: str):
	return txt.replace("<p>", "").replace("</p>", "").replace("<hon>", "").replace("<hoff>", "")

	def _split_generators(self, dl_manager: DownloadManager):
	"""Returns SplitGenerators."""
	filepaths = {}
	for lang in ["en", "fr"]:
	for ftype in ["context", "current"]:
	fname = f"{self.config.name}.{ftype}.{lang}"
	filepaths[f"{ftype}_{lang}"] = dl_manager.download_and_extract(f"{_URL}/{fname}")
	filepaths["contrast_fr"] = dl_manager.download_and_extract(f"{_URL}/{self.config.name}.contrast.fr")
	filepaths["type"] = dl_manager.download_and_extract(f"{_URL}/{self.config.name}.type")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepaths": filepaths,
	"cfg_name": self.config.name,
	},
	)
	]

	def _generate_examples(self, filepaths: Dict[str, str], cfg_name: str):
	"""Yields examples as (key, example) tuples."""

	with open(filepaths["current_en"]) as f:
	current_en = f.read().splitlines()
	with open(filepaths["current_fr"]) as f:
	current_fr = f.read().splitlines()
	with open(filepaths["context_en"]) as f:
	context_en = f.read().splitlines()
	with open(filepaths["context_fr"]) as f:
	context_fr = f.read().splitlines()
	with open(filepaths["contrast_fr"]) as f:
	contrast_fr = f.read().splitlines()
	with open(filepaths["type"]) as f:
	alltyp = f.read().splitlines()
	for i, (curr_en, curr_fr, ctx_en, ctx_fr, con_fr, typ) in enumerate(
	zip(current_en, current_fr, context_en, context_fr, contrast_fr, alltyp)
	):
	yield i, {
	"id": i,
	"context_en": self.clean_string(ctx_en),
	"en": self.clean_string(curr_en),
	"context_fr": self.clean_string(ctx_fr),
	"fr": self.clean_string(curr_fr),
	"contrast_fr": self.clean_string(con_fr),
	"context_en_with_tags": ctx_en,
	"en_with_tags": curr_en,
	"context_fr_with_tags": ctx_fr,
	"fr_with_tags": curr_fr,
	"contrast_fr_with_tags": con_fr,
	"type": typ,
	}