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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.
"""MERLIN Written Learner Corpus for Czech, German, Italian 1.1."""
import csv
import json
import os
import datasets
_CITATION = """\
@inproceedings{boyd-etal-2014-merlin,
title = "The {MERLIN} corpus: Learner language and the {CEFR}",
author = {Boyd, Adriane and
Hana, Jirka and
Nicolas, Lionel and
Meurers, Detmar and
Wisniewski, Katrin and
Abel, Andrea and
Sch{\"o}ne, Karin and
{\v{S}}tindlov{\'a}, Barbora and
Vettori, Chiara},
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/606_Paper.pdf",
pages = "1281--1288",
abstract = "The MERLIN corpus is a written learner corpus for Czech, German,and Italian that has been designed to illustrate the Common European Framework of Reference for Languages (CEFR) with authentic learner data. The corpus contains 2,290 learner texts produced in standardized language certifications covering CEFR levels A1-C1. The MERLIN annotation scheme includes a wide range of language characteristics that enable research into the empirical foundations of the CEFR scales and provide language teachers, test developers, and Second Language Acquisition researchers with concrete examples of learner performance and progress across multiple proficiency levels. For computational linguistics, it provide a range of authentic learner data for three target languages, supporting a broadening of the scope of research in areas such as automatic proficiency classification or native language identification. The annotated corpus and related information will be freely available as a corpus resource and through a freely accessible, didactically-oriented online platform.",
}
"""
_DESCRIPTION = """\
The MERLIN corpus is a written learner corpus for Czech, German, and Italian that has been
designed to illustrate the Common European Framework of Reference for Languages (CEFR) with
authentic learner data. The corpus contains learner texts produced in standardized language
certifications covering CEFR levels A1-C1. The MERLIN annotation scheme includes a wide
range of language characteristics that provide researchers with concrete examples of learner
performance and progress across multiple proficiency levels.
"""
_HOMEPAGE = "https://merlin-platform.eu/"
_LICENSE = "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)"
_URLS = {
"multilingual": "https://clarin.eurac.edu/repository/xmlui/bitstream/handle/20.500.12124/6/merlin-text-v1.1.zip",
"german": "https://clarin.eurac.edu/repository/xmlui/bitstream/handle/20.500.12124/6/merlin-text-v1.1.zip",
"italian": "https://clarin.eurac.edu/repository/xmlui/bitstream/handle/20.500.12124/6/merlin-text-v1.1.zip",
"czech": "https://clarin.eurac.edu/repository/xmlui/bitstream/handle/20.500.12124/6/merlin-text-v1.1.zip",
}
class MerlinDataset(datasets.GeneratorBasedBuilder):
"""Merlin dataset including three languages."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="multilingual", version=VERSION, description="Merlin dataset including three languages."),
datasets.BuilderConfig(name="german", version=VERSION, description="Merlin dataset German."),
datasets.BuilderConfig(name="italian", version=VERSION, description="Merlin dataset Italian."),
datasets.BuilderConfig(name="czech", version=VERSION, description="Merlin dataset Czech."),
]
def _info(self):
features = datasets.Features(
{
"author": datasets.Value("string"),
"language": datasets.ClassLabel(num_classes=3, names=["Czech", "German", "Italian"]),
"level": datasets.ClassLabel(num_classes=6, names=['A1', 'A2', 'B1', 'B2', 'C1', 'C2']),
"level_grammar": datasets.ClassLabel(num_classes=6, names=['A1', 'A2', 'B1', 'B2', 'C1', 'C2']),
"level_ortography": datasets.ClassLabel(num_classes=6, names=['A1', 'A2', 'B1', 'B2', 'C1', 'C2']),
"level_vocabulary_range": datasets.ClassLabel(num_classes=6, names=['A1', 'A2', 'B1', 'B2', 'C1', 'C2']),
"level_vocabulary_control": datasets.ClassLabel(num_classes=6, names=['A1', 'A2', 'B1', 'B2', 'C1', 'C2']),
"level_coherence": datasets.ClassLabel(num_classes=6, names=['A1', 'A2', 'B1', 'B2', 'C1', 'C2']),
"level_appropriateness": datasets.ClassLabel(num_classes=6, names=['A1', 'A2', 'B1', 'B2', 'C1', 'C2']),
"text": datasets.Value("string"),
"text_target": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _URLS[self.config.name]
data_dir = dl_manager.download_and_extract(urls)
filepath = os.path.join(data_dir, "merlin-text-v1.1/meta_ltext_THs")
if self.config.name != "multilingual":
filepath = os.path.join(filepath, self.config.name)
print(f"Genereting split from {filepath}")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": filepath,
"split": "train",
},
),
]
def _generate_examples(self, filepath, split):
import re
file_list = []
for path, _, files in os.walk(filepath):
for file in files:
file_list.append(os.path.join(path, file))
print(f"Reading {len(file_list)} files")
# Transform the data
for f in file_list:
raw_text = open(f, "r").read()
language = re.findall(r'(Test language: )(.*?)(\n)', raw_text)[0][1]
author_id = re.findall(r'(Author ID: )(.*?)(\n)', raw_text)[0][1]
level = re.findall(r'(CEFR level of test: )(.*?)(\n)', raw_text)[0][1]
level_grammar = re.findall(r'(Grammatical accuracy: )(.*?)(\n)', raw_text)[0][1]
level_ortography = re.findall(r'(Orthography: )(.*?)(\n)', raw_text)[0][1]
level_vocabulary_range = re.findall(r'(Vocabulary range: )(.*?)(\n)', raw_text)[0][1]
level_vocabulary_control = re.findall(r'(Vocabulary control: )(.*?)(\n)', raw_text)[0][1]
level_coherence = re.findall(r'(Coherence/Cohesion: )(.*?)(\n)', raw_text)[0][1]
level_appropriateness = re.findall(r'(Sociolinguistic appropriateness: )(.*?)(\n)', raw_text)[0][1]
text = re.findall(r'(Learner text: \n\n)(.*?)(\n\n----------------\n\n)', raw_text, re.DOTALL)[0][1]
text_target = re.findall(r'(Target hypothesis 1: \n\n)(.*?)(\n\n----------------\n\n)', raw_text, re.DOTALL)[0][1]
id_ = f'{language}_{author_id}'
yield id_, {
"author": author_id,
"language": language,
"level": level,
"level_grammar": level_grammar,
"level_ortography": level_ortography,
"level_vocabulary_range": level_vocabulary_range,
"level_vocabulary_control": level_vocabulary_control,
"level_coherence": level_coherence,
"level_appropriateness": level_appropriateness,
"text": text,
"text_target": text_target,
}
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