File size: 9,176 Bytes
ed2b8e8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 |
from pathlib import Path
from typing import Dict, List, Tuple
import re
import datasets
import pandas as pd
from nusacrowd.utils import schemas
from nusacrowd.utils.configs import NusantaraConfig
from nusacrowd.utils.constants import DEFAULT_NUSANTARA_VIEW_NAME, DEFAULT_SOURCE_VIEW_NAME, Tasks
_DATASETNAME = "nusatranslation_mt"
_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME
_LANGUAGES = ["ind", "btk", "bew", "bug", "jav", "mad", "mak", "min", "mui", "rej", "sun"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LOCAL = False
_CITATION = """\
@unpublished{anonymous2023nusawrites:,
title={NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages},
author={Anonymous},
journal={OpenReview Preprint},
year={2023},
note={anonymous preprint under review}
}
"""
_DESCRIPTION = """\
Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. While these methods have proven effective and cost-efficient, we have identified limitations in the resulting corpora, including a lack of lexical diversity and cultural relevance to local communities. To address this gap, we conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content. In addition, we present the NusaWrites benchmark, encompassing 12 underrepresented and extremely low-resource languages spoken by millions of individuals in Indonesia. Our empirical experiment results using existing multilingual large language models conclude the need to extend these models to more underrepresented languages.
We introduce a novel high quality human curated corpora, i.e., NusaMenulis, which covers 12 languages spoken in Indonesia. The resource extend the coverage of languages to 5 new languages, i.e., Ambon (abs), Bima (bhp), Makassarese (mak), Palembang / Musi (mui), and Rejang (rej).
For the rhetoric mode classification task, we cover 5 rhetoric modes, i.e., narrative, persuasive, argumentative, descriptive, and expository.
"""
_HOMEPAGE = "https://github.com/IndoNLP/nusatranslation/tree/main/datasets/mt"
_LICENSE = "Creative Commons Attribution Share-Alike 4.0 International"
_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
_SOURCE_VERSION = "1.0.0"
_NUSANTARA_VERSION = "1.0.0"
_URLS = {
"train": "https://raw.githubusercontent.com/IndoNLP/nusa-writes/main/data/nusa_kalimat-mt-{lang}-train.csv",
"validation": "https://raw.githubusercontent.com/IndoNLP/nusa-writes/main/data/nusa_kalimat-mt-{lang}-valid.csv",
"test": "https://raw.githubusercontent.com/IndoNLP/nusa-writes/main/data/nusa_kalimat-mt-{lang}-test.csv",
}
LANGUAGES_MAP = {
"abs": "ambon",
"btk": "batak",
"bew": "betawi",
"bhp": "bima",
"jav": "javanese",
"mad": "madurese",
"mak": "makassarese",
"min": "minangkabau",
"mui": "musi",
"rej": "rejang",
"sun": "sundanese",
}
class NusaTranslationMT(datasets.GeneratorBasedBuilder):
"""NusaTranslation-MT is a parallel corpus for training and benchmarking machine translation models from 11 Indonesian local language to Bahasa Indonesia. The data is presented in csv format with 2 columns, where one column contain sentence in Bahasa and another in the local language."""
BUILDER_CONFIGS = (
[
NusantaraConfig(
name=f"nusatranslation_mt_ind_{subset}_source",
version=datasets.Version(_SOURCE_VERSION),
description=f"nusatranslation_mt ind2{subset} source schema",
schema="source",
subset_id=f"nusatranslation_mt",
)
for subset in _LANGUAGES[1:]
]
+ [
NusantaraConfig(
name=f"nusatranslation_mt_ind_{subset}_nusantara_t2t",
version=datasets.Version(_NUSANTARA_VERSION),
description=f"nusatranslation_mt ind2{subset} Nusantara schema",
schema="nusantara_t2t",
subset_id=f"nusatranslation_mt",
)
for subset in _LANGUAGES[1:]
]
+ [
NusantaraConfig(
name=f"nusatranslation_mt_{subset}_ind_source",
version=datasets.Version(_SOURCE_VERSION),
description=f"nusatranslation_mt {subset}2ind source schema",
schema="source",
subset_id=f"nusatranslation_mt",
)
for subset in _LANGUAGES[1:]
]
+ [
NusantaraConfig(
name=f"nusatranslation_mt_{subset}_ind_nusantara_t2t",
version=datasets.Version(_NUSANTARA_VERSION),
description=f"nusatranslation_mt {subset}2ind Nusantara schema",
schema="nusantara_t2t",
subset_id=f"nusatranslation_mt",
)
for subset in _LANGUAGES[1:]
]
)
DEFAULT_CONFIG_NAME = "nusatranslation_mt_jav_ind_source"
def _info(self):
if self.config.schema == "source":
features = datasets.Features({"id": datasets.Value("string"), "text": datasets.Value("string"), "label": datasets.Value("string")})
elif self.config.schema == "nusantara_t2t":
features = schemas.text2text_features
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 self.config.name.split("_")[2] != "ind" else self.config.name.split("_")[3]
train_csv_path = Path(dl_manager.download_and_extract(_URLS["train"].format(lang=lang)))
validation_csv_path = Path(dl_manager.download_and_extract(_URLS["validation"].format(lang=lang)))
test_csv_path = Path(dl_manager.download_and_extract(_URLS["test"].format(lang=lang)))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": train_csv_path},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": validation_csv_path},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": test_csv_path},
),
]
def _merge_subsets(self, df, subsets, revert=False):
if not subsets:
return None
# df = None
# print(dfs)
# print(subsets)
orig_columns = df.columns.tolist()
print(df.columns)
df.columns = orig_columns[:1] + ["label", "text"] if revert else orig_columns[:1] + ["text", "label"]
return df
def get_domain_data(self, dfs):
domain = self.config.name
matched_domain = re.findall(r"nusatranslation_mt_.*?_.*?_", domain)
assert len(matched_domain) == 1
domain = matched_domain[0][:-1].replace("nusatranslation_mt_", "").split("_")
src_lang, tgt_lang = domain[0], domain[1]
subsets = LANGUAGES_MAP.get(src_lang if src_lang != "ind" else tgt_lang, None)
return src_lang, tgt_lang, self._merge_subsets(dfs, subsets, revert=(src_lang != "ind"))
def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
if self.config.schema != "source" and self.config.schema != "nusantara_t2t":
raise ValueError(f"Invalid config schema: {self.config.schema}")
# print(filepath)
df = pd.read_csv(filepath)
# ldf = []
# for fp in filepath:
# ldf.append(pd.read_csv(fp))
src_lang, tgt_lang, df = self.get_domain_data((df))
if self.config.schema == "source":
for idx, row in enumerate(df.itertuples()):
ex = {
"id": str(idx),
"text": row.text,
"label": row.label,
}
yield idx, ex
elif self.config.schema == "nusantara_t2t":
for idx, row in enumerate(df.itertuples()):
ex = {
"id": str(idx),
"text_1": row.text,
"text_2": row.label,
"text_1_name": src_lang,
"text_2_name": tgt_lang,
}
yield idx, ex
else:
raise ValueError(f"Invalid config: {self.config.name}")
|