reader & data

zulu_stance.py

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+# coding=utf-8
+# Copyright 2020 HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""
+
+import json
+import os
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{dlamini_zulu_stance,
+  title={Bridging the Domain Gap for Stance Detection for the Zulu language},
+  author={Dlamini, Gcinizwe and Bekkouch, Imad Eddine Ibrahim and Khan, Adil and Derczynski, Leon},
+  booktitle={Proceedings of IEEE IntelliSys},
+  year={2022}
+}
+"""
+
+_DESCRIPTION = """\
+Misinformation has become a major concern in recent last years given its 
+spread across our information sources. In the past years, many NLP tasks have
+been introduced in this area, with some systems reaching good results on 
+English language datasets. Existing AI based approaches for fighting 
+misinformation in literature suggest automatic stance detection as an integral
+first step to success. Our paper aims at utilizing this progress made for
+English to transfers that knowledge into other languages, which is a 
+non-trivial task due to the domain gap between English and the target 
+languages. We propose a black-box non-intrusive method that utilizes techniques
+from Domain Adaptation to reduce the domain gap, without requiring any human
+expertise in the target language, by leveraging low-quality data in both a
+supervised and unsupervised manner. This allows us to rapidly achieve similar
+results for stance detection for the Zulu language, the target language in
+this work, as are found for English. We also provide a stance detection dataset
+in the Zulu language. 
+"""
+
+_URL = "ZUstance.json"
+
+
+class ZuluStanceConfig(datasets.BuilderConfig):
+    """BuilderConfig for ZuluStance"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig ZuluStance.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(ZuluStanceConfig, self).__init__(**kwargs)
+
+
+class ZuluStance(datasets.GeneratorBasedBuilder):
+    """ZuluStance dataset."""
+
+    BUILDER_CONFIGS = [
+        ZuluStanceConfig(name="zulu-stance", version=datasets.Version("1.0.0"), description="Stance dataset in Zulu"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "target": datasets.Value("string"),
+                    "stance": datasets.features.ClassLabel(
+                        names=[
+                            "FAVOR",
+                            "AGAINST",
+                            "NONE",
+                        ]
+                    )
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://arxiv.org/abs/2205.03153",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        downloaded_file = dl_manager.download_and_extract(_URL)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            zustance_dataset = json.load(f)
+            for instance in zustance_dataset:
+                instance["id"] = str(guid)
+                instance["text"] = instance.pop("Tweet")
+                instance["target"] = instance.pop("Target")
+                instance["stance"] = instance.pop("Stance")
+                yield guid, instance
+                guid += 1