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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
# TODO: Address all TODOs and remove all explanatory comments
""" Multilexnorm dataset."""
import csv
import json
import os
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = r"""\
@inproceedings{van-der-goot-etal-2021-multilexnorm,
title = "{M}ulti{L}ex{N}orm: A Shared Task on Multilingual Lexical Normalization",
author = {van der Goot, Rob and
Ramponi, Alan and
Zubiaga, Arkaitz and
Plank, Barbara and
Muller, Benjamin and
San Vicente Roncal, I{\~n}aki and
Ljube{\v{s}}i{\'c}, Nikola and
{\c{C}}etino{\u{g}}lu, {\"O}zlem and
Mahendra, Rahmad and
{\c{C}}olako{\u{g}}lu, Talha and
Baldwin, Timothy and
Caselli, Tommaso and
Sidorenko, Wladimir},
booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wnut-1.55",
doi = "10.18653/v1/2021.wnut-1.55",
pages = "493--509",
abstract = "Lexical normalization is the task of transforming an utterance into its standardized form. This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation. Such variation is typical for social media on which information is shared in a multitude of ways, including diverse languages and code-switching. Since the seminal work of Han and Baldwin (2011) a decade ago, lexical normalization has attracted attention in English and multiple other languages. However, there exists a lack of a common benchmark for comparison of systems across languages with a homogeneous data and evaluation setup. The MultiLexNorm shared task sets out to fill this gap. We provide the largest publicly available multilingual lexical normalization benchmark including 13 language variants. We propose a homogenized evaluation setup with both intrinsic and extrinsic evaluation. As extrinsic evaluation, we use dependency parsing and part-of-speech tagging with adapted evaluation metrics (a-LAS, a-UAS, and a-POS) to account for alignment discrepancies. The shared task hosted at W-NUT 2021 attracted 9 participants and 18 submissions. The results show that neural normalization systems outperform the previous state-of-the-art system by a large margin. Downstream parsing and part-of-speech tagging performance is positively affected but to varying degrees, with improvements of up to 1.72 a-LAS, 0.85 a-UAS, and 1.54 a-POS for the winning system.",
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
For this task, participants are asked to develop a system that performs lexical normalization: the conversion of non-canonical texts to their canonical equivalent form. In particular, this task includes data from 12 languages.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "http://noisy-text.github.io/2021/multi-lexnorm.html"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "Creative Commons Attribution 4.0 International License."
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_DATA_DIR = "data"
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class MultiLexNorm(datasets.GeneratorBasedBuilder):
""" Lexnorm dataset for 12 languages."""
VERSION = datasets.Version("1.1.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="da", version=VERSION, description="Danish"),
datasets.BuilderConfig(name="de", version=VERSION, description="German"),
datasets.BuilderConfig(name="en", version=VERSION, description="English"),
datasets.BuilderConfig(name="es", version=VERSION, description="Spanish"),
datasets.BuilderConfig(name="hr", version=VERSION, description="Croatian"),
datasets.BuilderConfig(name="id-en", version=VERSION, description="Indonesian-English"),
datasets.BuilderConfig(name="it", version=VERSION, description="Italian"),
datasets.BuilderConfig(name="nl", version=VERSION, description="Dutch"),
datasets.BuilderConfig(name="sl", version=VERSION, description="Slovenian"),
datasets.BuilderConfig(name="sr", version=VERSION, description="Serbian"),
datasets.BuilderConfig(name="tr", version=VERSION, description="Turkish"),
datasets.BuilderConfig(name="tr-de", version=VERSION, description="Turkish-German"),
]
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
features = datasets.Features(
{
"inputs": datasets.Value("string"),
"targets": datasets.Value("string"),
# These are the features of your dataset like images, labels ...
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
try:
_URLS = {
"train": os.path.join(_DATA_DIR, self.config.name, "train.norm"),
"dev": os.path.join(_DATA_DIR, self.config.name, "dev.norm"),
"test": os.path.join(_DATA_DIR, self.config.name, "test.norm"),
}
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["dev"],
"split": "dev",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["test"],
"split": "test",
},
),
]
except FileNotFoundError:
_URLS = {
"train": os.path.join(_DATA_DIR, self.config.name, "train.norm"),
"test": os.path.join(_DATA_DIR, self.config.name, "test.norm"),
}
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["test"],
"split": "test",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
with open(filepath, encoding="utf-8") as f:
for key, line in enumerate(f):
if len(line) > 1:
ip, tgt = line.split("\t")
else: # blank
ip, tgt = "", ""
yield key, {
"inputs": ip.strip(),
"targets": tgt.strip(),
}
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