Datasets:
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# 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,
}
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