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Register a new userWANLP 2021 Shared-Task: Towards Irony and Sentiment Detection in Arabic Tweets using Multi-headed-LSTM-CNN-GRU and MaRBERT
Wanlp 2021 المهمة المشتركة: نحو كشف المفارقة والشعور في تغريدات عربية باستخدام Multi-head-lstm-cnn-gru و marbert
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المفارقة والكشف عن المعنويات مهمة لفهم سلوك الناس وأفكار الناس.وبالتالي أصبحت مهمة شعبية في معالجة اللغة الطبيعية (NLP).هذه الورقة تقدم النتائج والنتائج الرئيسية في المهام المشتركة WANLP 2021 واحدة واثنين.استندت المهمة إلى DataSet Arsarcasm-V2 (أبو فرحة وآخرون، 2021).في هذه الورقة، نحن نصف نظامنا متعدد الرؤوس LSTM-CNN-GRU وكذلك ماربرت (عبد المجيد وآخرون، 2021) مقدم لهذه المهمة المشتركة، المرتبة 10 من أصل 27 في مهمة مشتركة تحقيق واحد 0.5662 F1-Sarcasmوتحتل المرتبة 3 من 22 في المهمة المشتركة اثنين من تحقيق 0.7321 F1-PN تحت اسم مستخدم Codalab Rematchka ''.لقد جربنا نماذج مختلفة، وهناك نماذج أفضل أداء هي مجموعة من cnn-lstm متعددة برأسنا، حيث استخدمنا نص prepossessed و emoji المقدمة من تغريدات وماربرت.
Irony and Sentiment detection is important to understand people's behavior and thoughts. Thus it has become a popular task in natural language processing (NLP). This paper presents results and main findings in WANLP 2021 shared tasks one and two. The task was based on the ArSarcasm-v2 dataset (Abu Farha et al., 2021). In this paper, we describe our system Multi-headed-LSTM-CNN-GRU and also MARBERT (Abdul-Mageed et al., 2021) submitted for the shared task, ranked 10 out of 27 in shared task one achieving 0.5662 F1-Sarcasm and ranked 3 out of 22 in shared task two achieving 0.7321 F1-PN under CodaLab username rematchka''. We experimented with various models and the two best performing models are a Multi-headed CNN-LSTM-GRU in which we used prepossessed text and emoji presented from tweets and MARBERT.
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This paper provides an overview of the WANLP 2021 shared task on sarcasm and sentiment detection in Arabic. The shared task has two subtasks: sarcasm detection (subtask 1) and sentiment analysis (subtask 2). This shared task aims to promote and bring
attention to Arabic sarcasm detection, which is crucial to improve the performance in other tasks such as sentiment analysis. The dataset used in this shared task, namely ArSarcasm-v2, consists of 15,548 tweets labelled for sarcasm, sentiment and dialect. We received 27 and 22 submissions for subtasks 1 and 2 respectively. Most of the approaches relied on using and fine-tuning pre-trained language models such as AraBERT and MARBERT. The top achieved results for the sarcasm detection and sentiment analysis tasks were 0.6225 F1-score and 0.748 F1-PN respectively.
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This paper presents our strategy to tackle the EACL WANLP-2021 Shared Task 2: Sarcasm and Sentiment Detection. One of the subtasks aims at developing a system that identifies whether a given Arabic tweet is sarcastic in nature or not, while the other
aims to identify the sentiment of the Arabic tweet. We approach the task in two steps. The first step involves pre processing the provided dataset by performing insertions, deletions and segmentation operations on various parts of the text. The second step involves experimenting with multiple variants of two transformer based models, AraELECTRA and AraBERT. Our final approach was ranked seventh and fourth in the Sarcasm and Sentiment Detection subtasks respectively.
We use the MacBERT transformers and fine-tune them to ROCLING-2021 shared tasks using the CVAT and CVAS data. We compare the performance of MacBERT with the other two transformers BERT and RoBERTa in the valence and arousal dimensions, respectively.
MAE and correlation coefficient (r) were used as evaluation metrics. On ROCLING-2021 test set, our used MacBERT model achieves 0.611 of MAE and 0.904 of r in the valence dimensions; and 0.938 of MAE and 0.549 of r in the arousal dimension.
Sarcasm is one of the main challenges for sentiment analysis systems due to using implicit indirect phrasing for expressing opinions, especially in Arabic. This paper presents the system we submitted to the Sarcasm and Sentiment Detection task of WAN
LP-2021 that is capable of dealing with both two subtasks. We first perform fine-tuning on two kinds of pre-trained language models (PLMs) with different training strategies. Then an effective stacking mechanism is applied on top of the fine-tuned PLMs to obtain the final prediction. Experimental results on ArSarcasm-v2 dataset show the effectiveness of our method and we rank third and second for subtask 1 and 2.
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