# Mawqif: A Multi-label Arabic Dataset for Target-specific Stance Detection - This repository contains the data and classifier used in the paper titled "Mawqif: A Multi-label Arabic Dataset for Target-specific Stance Detection" accepted to appear at [WANLP](https://sites.google.com/view/wanlp2022/home?authuser=0), [EMNLP 2022](https://2022.emnlp.org) . **Link to the paper:** (https://preview.aclanthology.org/emnlp-22-ingestion/2022.wanlp-1.16/) - *Mawqif* is the first Arabic dataset that can be used for target-specific stance detection. - This is a multi-label dataset where each data point is annotated for stance, sentiment, and sarcasm, which will provide a benchmark for the three tasks. It can also provide more opportunities for studying the interaction between different opinion dimensions and evaluating a multi-task model. - We benchmark *Mawqif* dataset on the stance detection task and evaluate the performance of four BERT-based models. Our best model achieves a macro-F1 of 78.89\%, which shows that there is ample room for improvement on this challenging task. - In addition to the annotated tweets, we also release the **annotation guidelines**, and the **code** used to build a standard pipeline under the [PyTorch Lightning](https://www.pytorchlightning.ai) framework to fine-tune BERT-based models for stance detection. # Mawqif Statistics - This dataset consists of 4,121 tweets in multi-dialectal Arabic. Each tweet is annotated with a stance toward one of three targets: “COVID-19 vaccine,” “digital transformation,” and “women empowerment.” In addition, it is annotated with sentiment and sarcasm polarities. - The following figure illustrates the labels’ distribution across all targets, and the distribution per target. dataStat-2 # Interactive Visualization To browse an interactive visualization of the *Mawqif* dataset, please click [here](https://public.tableau.com/views/MawqifDatasetDashboard/Dashboard1?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link) - *You can click on visualization components to filter the data by target and by class. **For example,** you can click on “women empowerment" and "against" to get the information of tweets that express against women empowerment.* # Citation If you feel our paper and resources are useful, please consider citing our work! ``` TBA ```