Closes SEACrowd#14 | Create dataset loader for ara-close-lange (SEACr…

…owd#243)

* Add ara_close dataloader

* Rename class name to AraCloseDataset

seacrowd/sea_datasets/ara_close/ara_close.py

@@ -0,0 +1,194 @@
+# coding=utf-8
+# Copyright 2022 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.
+
+""" \
+The dataset contribution of this study is a compilation of short fictional stories \
+written in Bikol for readability assessment. The data was combined other collected \
+Philippine language corpora, such as Tagalog and Cebuano. The data from these languages \
+are all distributed across the Philippine elementary system's first three grade \
+levels (L1, L2, L3). We sourced this dataset from Let's Read Asia (LRA), Bloom Library, \
+Department of Education, and Adarna House.
+"""
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{imperial-kochmar-2023-automatic,
+    title = "Automatic Readability Assessment for Closely Related Languages",
+    author = "Imperial, Joseph Marvin  and
+      Kochmar, Ekaterina",
+    editor = "Rogers, Anna  and
+      Boyd-Graber, Jordan  and
+      Okazaki, Naoaki",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.findings-acl.331",
+    doi = "10.18653/v1/2023.findings-acl.331",
+    pages = "5371--5386",
+    abstract = "In recent years, the main focus of research on automatic readability assessment (ARA) \
+    has shifted towards using expensive deep learning-based methods with the primary goal of increasing models{'} accuracy. \
+    This, however, is rarely applicable for low-resource languages where traditional handcrafted features are still \
+    widely used due to the lack of existing NLP tools to extract deeper linguistic representations. In this work, \
+    we take a step back from the technical component and focus on how linguistic aspects such as mutual intelligibility \
+    or degree of language relatedness can improve ARA in a low-resource setting. We collect short stories written in three \
+    languages in the Philippines{---}Tagalog, Bikol, and Cebuano{---}to train readability assessment models and explore the \
+    interaction of data and features in various cross-lingual setups. Our results show that the inclusion of CrossNGO, \
+    a novel specialized feature exploiting n-gram overlap applied to languages with high mutual intelligibility, \
+    significantly improves the performance of ARA models compared to the use of off-the-shelf large multilingual \
+    language models alone. Consequently, when both linguistic representations are combined, we achieve state-of-the-art \
+    results for Tagalog and Cebuano, and baseline scores for ARA in Bikol.",
+}
+"""
+
+_DATASETNAME = "ara_close"
+
+_DESCRIPTION = """\
+The dataset contribution of this study is a compilation of short fictional stories \
+written in Bikol for readability assessment. The data was combined other collected \
+Philippine language corpora, such as Tagalog and Cebuano. The data from these languages \
+are all distributed across the Philippine elementary system's first three grade \
+levels (L1, L2, L3). We sourced this dataset from Let's Read Asia (LRA), Bloom Library, \
+Department of Education, and Adarna House. \
+"""
+
+_HOMEPAGE = "https://github.com/imperialite/ara-close-lang"
+
+_LANGUAGES = ["bcl", "ceb"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_LICENSE = Licenses.CC_BY_4_0.value  # example: Licenses.MIT.value, Licenses.CC_BY_NC_SA_4_0.value, Licenses.UNLICENSE.value, Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    "bcl": "https://raw.githubusercontent.com/imperialite/ara-close-lang/main/data/bikol/bik_all_data.txt",
+    # 'tgl': '', # file for tgl language was deleted
+    "ceb": "https://raw.githubusercontent.com/imperialite/ara-close-lang/main/data/cebuano/ceb_all_data.txt",
+}
+
+_SUPPORTED_TASKS = [Tasks.READABILITY_ASSESSMENT]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "1.0.0"
+
+
+class AraCloseDataset(datasets.GeneratorBasedBuilder):
+    f"""{_DESCRIPTION}"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [SEACrowdConfig(name=f"{_DATASETNAME}_{lang}_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME} source schema", schema="source", subset_id=f"{_DATASETNAME}",) for lang in _LANGUAGES] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{lang}_seacrowd_text",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_text",
+            subset_id=f"{_DATASETNAME}",
+        )
+        for lang in _LANGUAGES
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "title": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "label": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features(["1", "2", "3"])
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        lang = self.config.name.split("_")[2]
+        if lang in _LANGUAGES:
+            data_path = Path(dl_manager.download_and_extract(_URLS[lang]))
+        else:
+            data_path = [Path(dl_manager.download_and_extract(_URLS[lang])) for lang in _LANGUAGES]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_path,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        lang = self.config.name.split("_")[2]
+        if lang in _LANGUAGES:
+            file_content = open(filepath, "r").readlines()
+        else:
+            file_content = []
+            for path in filepath:
+                lines = open(path, "r").readlines()
+                file_content.extend(lines)
+
+        if self.config.schema == "source":
+            idx = 0
+            for line in file_content:
+                split_data = line.strip().split(",")
+                title = split_data[0]
+                label = split_data[1]
+                text = ",".join(split_data[2:])
+                ex = {"title": title, "text": text, "label": label}
+                yield idx, ex
+                idx += 1
+
+        elif self.config.schema == "seacrowd_text":
+            idx = 0
+            for line in file_content:
+                split_data = line.strip().split(",")
+                title = split_data[0]
+                label = split_data[1]
+                text = ",".join(split_data[2:])
+                ex = {
+                    "id": idx,
+                    "text": text,
+                    "label": label,
+                }
+                yield idx, ex
+                idx += 1
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")