| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| """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, |
| } |
