Datasets:

inseq
/

scat

	# coding=utf-8
	# 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.
	"""SCAT: Supporting Context for Ambiguous Translations"""

	import re
	from pathlib import Path
	from typing import Dict

	import datasets
	from datasets.utils.download_manager import DownloadManager


	_CITATION = """\
	@inproceedings{yin-etal-2021-context,
	title = "Do Context-Aware Translation Models Pay the Right Attention?",
	author = "Yin, Kayo and
	Fernandes, Patrick and
	Pruthi, Danish and
	Chaudhary, Aditi and
	Martins, Andr{\'e} F. T. and
	Neubig, Graham",
	booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
	month = aug,
	year = "2021",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2021.acl-long.65",
	doi = "10.18653/v1/2021.acl-long.65",
	pages = "788--801",
	}
	"""

	_DESCRIPTION = """\
	The Supporting Context for Ambiguous Translations corpus (SCAT) is a dataset
	of English-to-French translations annotated with human rationales used for resolving ambiguity
	in pronoun anaphora resolution for multi-sentence translation.
	"""

	_URL = "https://huggingface.co/datasets/inseq/scat/raw/main/filtered_scat"

	_HOMEPAGE = "https://github.com/neulab/contextual-mt/tree/master/data/scat"

	_LICENSE = "Unknown"

	class ScatConfig(datasets.BuilderConfig):
	def __init__(
	self,
	source_language: str,
	target_language: str,
	**kwargs
	):
	"""BuilderConfig for MT-GenEval.
	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 Scat(datasets.GeneratorBasedBuilder):

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [ScatConfig(name="sentences", source_language="en", target_language="fr")]

	DEFAULT_CONFIG_NAME = "sentences"

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

	@staticmethod
	def swap_pronoun(txt: str):
	pron: str = re.findall(r"<p>([^<]*)</p>", txt)[0]
	new_pron = pron
	is_cap = pron.istitle()
	if pron.lower() == "elles":
	new_pron = "ils"
	if pron.lower() == "elle":
	new_pron = "il"
	if pron.lower() == "ils":
	new_pron = "elles"
	if pron.lower() == "il":
	new_pron = "elle"
	if pron.lower() == "un":
	new_pron = "une"
	if pron.lower() == "une":
	new_pron = "un"
	if is_cap:
	new_pron = new_pron.capitalize()
	return txt.replace(f"<p>{pron}</p>", f"<p>{new_pron}</p>")

	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"),
	"has_supporting_context": datasets.Value("bool"),
	"has_supporting_preceding_context": datasets.Value("bool"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager: DownloadManager):
	"""Returns SplitGenerators."""
	filepaths = {}
	splits = ["train", "valid", "test"]
	for split in splits:
	filepaths[split] = {}
	for lang in ["en", "fr"]:
	for ftype in ["context", ""]:
	fname = f"filtered.{split}{'.' + ftype if ftype else ''}.{lang}"
	name = f"{ftype}_{lang}" if ftype else lang
	filepaths[split][name] = dl_manager.download_and_extract(f"{_URL}/{fname}")
	return [
	datasets.SplitGenerator(
	name=split_name,
	gen_kwargs={
	"filepaths": filepaths[split],
	},
	)
	for split, split_name in zip(splits, ["train", "validation", "test"])
	]


	def _generate_examples(
	self, filepaths: Dict[str, str]
	):
	""" Yields examples as (key, example) tuples. """
	with open(filepaths["en"]) as f:
	en = f.read().splitlines()
	with open(filepaths["fr"]) as f:
	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()
	for i, (e, f, ce, cf) in enumerate(zip(en, fr, context_en, context_fr)):
	allfields = " ".join([e, f, ce, cf])
	has_supporting_context = False
	if "<hon>" in allfields and "<hoff>" in allfields:
	has_supporting_context = True
	contrast_fr = self.swap_pronoun(f)
	yield i, {
	"id": i,
	"context_en": self.clean_string(ce),
	"en": self.clean_string(e),
	"context_fr": self.clean_string(cf),
	"fr": self.clean_string(f),
	"contrast_fr": self.clean_string(contrast_fr),
	"context_en_with_tags": ce,
	"en_with_tags": e,
	"context_fr_with_tags": cf,
	"fr_with_tags": f,
	"contrast_fr_with_tags": contrast_fr,
	"has_supporting_context": has_supporting_context,
	"has_supporting_preceding_context": "<hon>" in cf,
	}