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--- |
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annotations_creators: |
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- expert-generated |
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language_creators: |
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- found |
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language: |
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- zu |
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license: |
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- cc-by-4.0 |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 1K<n<10K |
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source_datasets: |
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- original |
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task_categories: |
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- text-classification |
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task_ids: |
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- fact-checking |
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- sentiment-classification |
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paperswithcode_id: zulu-stance |
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pretty_name: ZUstance |
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tags: |
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- stance-detection |
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--- |
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# Dataset Card for "zulu-stance" |
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## Table of Contents |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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## Dataset Description |
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- **Homepage:** [https://arxiv.org/abs/2205.03153](https://arxiv.org/abs/2205.03153) |
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- **Repository:** |
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- **Paper:** [https://arxiv.org/pdf/2205.03153](https://arxiv.org/pdf/2205.03153) |
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- **Point of Contact:** [Leon Derczynski](https://github.com/leondz) |
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- **Size of downloaded dataset files:** 212.54 KiB |
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- **Size of the generated dataset:** 186.76 KiB |
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- **Total amount of disk used:** 399.30KiB |
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### Dataset Summary |
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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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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. A natively-translated dataset is used for evaluation of domain transfer. |
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### Supported Tasks and Leaderboards |
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* |
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### Languages |
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Zulu (`bcp47:zu`) |
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## Dataset Structure |
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### Data Instances |
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#### zulu_stance |
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- **Size of downloaded dataset files:** 212.54 KiB |
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- **Size of the generated dataset:** 186.76 KiB |
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- **Total amount of disk used:** 399.30KiB |
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An example of 'train' looks as follows. |
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``` |
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{ |
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'id': '0', |
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'text': 'ubukhulu be-islam buba sobala lapho i-smartphone ifaka i-ramayana njengo-ramadan. #semst', |
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'target': 'Atheism', |
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'stance': 1} |
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``` |
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### Data Fields |
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- `id`: a `string` feature. |
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- `text`: a `string` expressing a stance. |
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- `target`: a `string` of the target/topic annotated here. |
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- `stance`: a class label representing the stance the text expresses towards the target. Full tagset with indices: |
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``` |
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0: "FAVOR", |
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1: "AGAINST", |
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2: "NONE", |
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``` |
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### Data Splits |
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| name |train| |
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|---------|----:| |
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|zulu_stance|1343 sentences| |
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## Dataset Creation |
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### Curation Rationale |
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To enable stance detection in Zulu and also to measure domain transfer in translation |
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### Source Data |
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#### Initial Data Collection and Normalization |
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The original data is taken from [Semeval2016 task 6: Detecting stance in tweets.](https://aclanthology.org/S16-1003/), |
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and then translated manually to Zulu. |
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#### Who are the source language producers? |
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English-speaking Twitter users. |
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### Annotations |
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#### Annotation process |
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See [Semeval2016 task 6: Detecting stance in tweets.](https://aclanthology.org/S16-1003/); the annotations are taken from there. |
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#### Who are the annotators? |
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See [Semeval2016 task 6: Detecting stance in tweets.](https://aclanthology.org/S16-1003/); the annotations are taken from there. |
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### Personal and Sensitive Information |
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The data was public at the time of collection. User names are preserved. |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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There's a risk of user-deleted content being in this data. The data has NOT been vetted for any content, so there's a risk of harmful text. |
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### Discussion of Biases |
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While the data is in Zulu, the source text is not from or about Zulu-speakers, and so still expresses the social biases and topics found in English-speaking Twitter users. Further, some of the topics are USA-specific. The sentiments and ideas in this dataset do not represent Zulu speakers. |
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### Other Known Limitations |
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The above limitations apply. |
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## Additional Information |
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### Dataset Curators |
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The dataset is curated by the paper's authors. |
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### Licensing Information |
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The authors distribute this data under Creative Commons attribution license, CC-BY 4.0. |
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### Citation Information |
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``` |
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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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### Contributions |
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Author-added dataset [@leondz](https://github.com/leondz) |
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