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mabl.py

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+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