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README.md

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 # 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/)
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 - *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.
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-- 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.
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-- 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.
+- This is a multi-label dataset where each data point is annotated for stance, sentiment, and sarcasm.
 
+- 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\%.
 
 
 
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 # Citation
 If you feel our paper and resources are useful, please consider citing our work!
 ```
-TBA
+Nora Saleh Alturayeif, Hamzah Abdullah Luqman, and Moataz Aly Kamaleldin Ahmed. 2022. Mawqif: A Multi-label Arabic Dataset for Target-specific Stance Detection. In Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP), pages 174–184, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
 ```