Datasets:

SEACrowd
/

mabl

	import csv
	from pathlib import Path
	from typing import Dict, List, Tuple

	import datasets
	from datasets.download.download_manager import DownloadManager

	from seacrowd.utils import schemas
	from seacrowd.utils.configs import SEACrowdConfig
	from seacrowd.utils.constants import Licenses, Tasks

	_CITATION = r"""
	@inproceedings{kabra-etal-2023-multi,
	title = "Multi-lingual and Multi-cultural Figurative Language Understanding",
	author = "Kabra, Anubha and
	Liu, Emmy and
	Khanuja, Simran and
	Aji, Alham Fikri and
	Winata, Genta and
	Cahyawijaya, Samuel and
	Aremu, Anuoluwapo and
	Ogayo, Perez and
	Neubig, Graham",
	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.525",
	doi = "10.18653/v1/2023.findings-acl.525",
	pages = "8269--8284",
	}
	"""

	_LOCAL = False
	_LANGUAGES = ["ind", "jav", "sun"]
	_DATASETNAME = "mabl"
	_DESCRIPTION = r"""\
	The MABL (Metaphors Across Borders and Languages) dataset is a collection of
	6,366 figurative language expressions from seven languages, crafted to improve
	multilingual models' understanding of figurative speech and its linguistic
	variations. It was built by crowdsourcing native speakers to generate paired
	metaphors that began with the same words but had different meanings, as well as
	the literal interpretations of both phrases. Each expression was checked by
	fluent speakers to ensure they were clear, appropriate, and followed the format,
	discarding any that didn't meet these standards.
	"""

	_HOMEPAGE = "https://github.com/simran-khanuja/Multilingual-Fig-QA"
	_LICENSE = Licenses.MIT.value
	_URL = "https://raw.githubusercontent.com/simran-khanuja/Multilingual-Fig-QA/main/langdata/"

	_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
	_SOURCE_VERSION = "1.0.0"
	_SEACROWD_VERSION = "2024.06.20"


	def iso3to2(lang: str) -> str:
	"""Convert 3-letter ISO code to its 2-letter equivalent"""
	iso_map = {"ind": "id", "jav": "jv", "sun": "su"}
	return iso_map[lang]


	class MABLDataset(datasets.GeneratorBasedBuilder):
	"""MABL dataset by Liu et al (2023)"""

	SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
	SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)

	SEACROWD_SCHEMA_NAME = "qa"

	dataset_names = sorted([f"{_DATASETNAME}_{lang}" for lang in _LANGUAGES])
	BUILDER_CONFIGS = []
	for name in dataset_names:
	source_config = SEACrowdConfig(
	name=f"{name}_source",
	version=SOURCE_VERSION,
	description=f"{_DATASETNAME} source schema",
	schema="source",
	subset_id=name,
	)
	BUILDER_CONFIGS.append(source_config)
	seacrowd_config = SEACrowdConfig(
	name=f"{name}_seacrowd_{SEACROWD_SCHEMA_NAME}",
	version=SEACROWD_VERSION,
	description=f"{_DATASETNAME} SEACrowd schema",
	schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
	subset_id=name,
	)
	BUILDER_CONFIGS.append(seacrowd_config)

	# Add configuration that allows loading all languages at once.
	BUILDER_CONFIGS.extend(
	[
	# mabl_source
	SEACrowdConfig(
	name=f"{_DATASETNAME}_source",
	version=SOURCE_VERSION,
	description=f"{_DATASETNAME} source schema (all)",
	schema="source",
	subset_id=_DATASETNAME,
	),
	# mabl_seacrowd_qa
	SEACrowdConfig(
	name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
	version=SEACROWD_VERSION,
	description=f"{_DATASETNAME} SEACrowd schema (all)",
	schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
	subset_id=_DATASETNAME,
	),
	]
	)

	DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"

	def _info(self) -> datasets.DatasetInfo:
	if self.config.schema == "source":
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"startphrase": datasets.Value("string"),
	"ending1": datasets.Value("string"),
	"ending2": datasets.Value("string"),
	"labels": datasets.Value("string"),
	}
	)
	elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
	features = schemas.qa_features
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
	"""Return SplitGenerators."""
	mabl_source_data = []
	languages = []

	lang = self.config.name.split("_")[1]
	if lang in _LANGUAGES:
	# Load data per language
	mabl_source_data.append(dl_manager.download_and_extract(_URL + f"{iso3to2(lang)}.csv"))
	languages.append(lang)
	else:
	# Load examples for all languages at once.
	# We run this block when mabl_source / mabl_seacrowd_qa was chosen.
	for lang in _LANGUAGES:
	mabl_source_data.append(dl_manager.download_and_extract(_URL + f"{iso3to2(lang)}.csv"))
	languages.append(lang)

	return [
	datasets.SplitGenerator(
	# The MABL paper mentions that due to the size of each subset,
	# they consider each split as a test set.
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepaths": mabl_source_data,
	"split": "test",
	"languages": languages,
	},
	)
	]

	def _generate_examples(self, filepaths: List[Path], split: str, languages: List[str]) -> Tuple[int, Dict]:
	"""Yield examples as (key, example) tuples"""

	startphrases = []
	endings1 = []
	endings2 = []
	labels = []

	for lang, filepath in zip(languages, filepaths):
	with open(filepath, encoding="utf-8") as f:
	csv_reader = csv.reader(f, delimiter=",")
	next(csv_reader, None) # skip the headers
	for row in csv_reader:
	# Unfortunately, the columns in the subfiles of the MABL
	# dataset are inconsistent. For 'ind', it is [ending1,
	# ending2, labels, startphrase]. But for 'jav' and 'sun',
	# the labels and startphrase columns were switched. Here,
	# I'm just hard-coding the column names
	if lang == "ind":
	end1, end2, label, start = row
	if lang == "jav" or lang == "sun":
	end1, end2, start, label = row

	startphrases.append(start)
	endings1.append(end1)
	endings2.append(end2)
	labels.append(label)

	for idx, (start, end1, end2, label) in enumerate(zip(startphrases, endings1, endings2, labels)):
	if self.config.schema == "source":
	example = {
	"id": str(idx),
	"startphrase": start,
	"ending1": end1,
	"ending2": end2,
	"labels": label,
	}
	elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
	# Create QA-specific items
	choices = [end1, end2]
	answer = choices[int(label)]

	# MABL doesn't differentiate between question and context.
	# It only contains a startphrase. Given that, I put the
	# startphrase in question and kept the context blank.
	example = {
	"id": str(idx),
	"question_id": idx,
	"document_id": idx,
	"question": start,
	"type": "multiple_choice",
	"choices": choices,
	"context": "",
	"answer": [answer],
	"meta": {},
	}

	yield idx, example