# 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("

", "").replace("

", "").replace("", "").replace("", "") @staticmethod def swap_pronoun(txt: str): pron: str = re.findall(r"

([^<]*)

", 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"

{pron}

", f"

{new_pron}

") 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"), } ) 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 "" in allfields and "" 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": "" in cf, }