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"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition""" |
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import json |
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import os |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@inproceedings{dlamini_zulu_stance, |
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title={Bridging the Domain Gap for Stance Detection for the Zulu language}, |
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author={Dlamini, Gcinizwe and Bekkouch, Imad Eddine Ibrahim and Khan, Adil and Derczynski, Leon}, |
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booktitle={Proceedings of IEEE IntelliSys}, |
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year={2022} |
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} |
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""" |
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_DESCRIPTION = """\ |
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This is a stance detection dataset in the Zulu language. The data is translated to Zulu by Zulu native speakers, from English source texts. |
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Misinformation has become a major concern in recent last years given its |
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spread across our information sources. In the past years, many NLP tasks have |
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been introduced in this area, with some systems reaching good results on |
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English language datasets. Existing AI based approaches for fighting |
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misinformation in literature suggest automatic stance detection as an integral |
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first step to success. Our paper aims at utilizing this progress made for |
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English to transfers that knowledge into other languages, which is a |
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non-trivial task due to the domain gap between English and the target |
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languages. We propose a black-box non-intrusive method that utilizes techniques |
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from Domain Adaptation to reduce the domain gap, without requiring any human |
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expertise in the target language, by leveraging low-quality data in both a |
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supervised and unsupervised manner. This allows us to rapidly achieve similar |
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results for stance detection for the Zulu language, the target language in |
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this work, as are found for English. We also provide a stance detection dataset |
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in the Zulu language. |
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""" |
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_URL = "ZUstance.json" |
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class ZuluStanceConfig(datasets.BuilderConfig): |
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"""BuilderConfig for ZuluStance""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig ZuluStance. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(ZuluStanceConfig, self).__init__(**kwargs) |
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class ZuluStance(datasets.GeneratorBasedBuilder): |
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"""ZuluStance dataset.""" |
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BUILDER_CONFIGS = [ |
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ZuluStanceConfig(name="zulu-stance", version=datasets.Version("1.0.0"), description="Stance dataset in Zulu"), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"target": datasets.Value("string"), |
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"stance": datasets.features.ClassLabel( |
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names=[ |
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"FAVOR", |
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"AGAINST", |
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"NONE", |
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] |
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) |
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} |
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), |
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supervised_keys=None, |
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homepage="https://arxiv.org/abs/2205.03153", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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downloaded_file = dl_manager.download_and_extract(_URL) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file}), |
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] |
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def _generate_examples(self, filepath): |
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logger.info("⏳ Generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as f: |
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guid = 0 |
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zustance_dataset = json.load(f) |
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for instance in zustance_dataset: |
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instance["id"] = str(guid) |
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instance["text"] = instance.pop("Tweet") |
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instance["target"] = instance.pop("Target") |
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instance["stance"] = instance.pop("Stance") |
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yield guid, instance |
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guid += 1 |
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