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Register a new userThe IDC System for Sentiment Classification and Sarcasm Detection in Arabic
نظام IDC لتصنيف المعنويات والكشف عن السخرية باللغة العربية
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تجذب تصنيف المعنويات والكشف عن السخرية الكثير من الاهتمام من قبل مجتمع البحوث NLP. ومع ذلك، فإن حل هاتين المشكلتين باللغة العربية وعلى أساس بيانات الشبكة الاجتماعية (I.E.، Twitter) لا يزال مصلحة أقل. في هذه الورقة نقدم حلولا مخصصة لتصنيف المعنويات ومهام الكشف عن السخرية التي تم تقديمها كجزء من مهمة مشتركة من قبل أبو فرحة وآخرون. (2021). نقوم بضبط نماذج المحولات الحالية المحولات الحالية لاحتياجاتنا. بالإضافة إلى ذلك، نستخدم مجموعة متنوعة من تقنيات التعلم الآلي مثل أخذ العينات الأولية والتكبير والتعبئة والتغليف واستخدام ميزات META لتحسين أداء النماذج. نحن نحقق درجة F1 من 0.75 على مشكلة تصنيف المعنويات حيث يتم حساب درجة F1 على الفصول الإيجابية والسلبية (لا يتم أخذ الفصل المحايد في الاعتبار). نحن نحقق درجة F1 من 0.66 فوق مشكلة الكشف عن السخرية حيث يتم حساب درجة F1 عبر الفئة الساخرة فقط. في كلتا الحالتين، يتم تقييم النتائج المذكورة أعلاه على Arsarcasm-V2 - مجموعة بيانات ممتدة من Arsarcasm (Farha و Magdy، 2020) تم تقديمها كجزء من المهمة المشتركة. هذا يعكس تحسنا لتحقيق أحدث النتائج في كلتا المهام.
Sentiment classification and sarcasm detection attract a lot of attention by the NLP research community. However, solving these two problems in Arabic and on the basis of social network data (i.e., Twitter) is still of lower interest. In this paper we present designated solutions for sentiment classification and sarcasm detection tasks that were introduced as part of a shared task by Abu Farha et al. (2021). We adjust the existing state-of-the-art transformer pretrained models for our needs. In addition, we use a variety of machine-learning techniques such as down-sampling, augmentation, bagging, and usage of meta-features to improve the models performance. We achieve an F1-score of 0.75 over the sentiment classification problem where the F1-score is calculated over the positive and negative classes (the neutral class is not taken into account). We achieve an F1-score of 0.66 over the sarcasm detection problem where the F1-score is calculated over the sarcastic class only. In both cases, the above reported results are evaluated over the ArSarcasm-v2--an extended dataset of the ArSarcasm (Farha and Magdy, 2020) that was introduced as part of the shared task. This reflects an improvement to the state-of-the-art results in both tasks.
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We describe our submitted system to the 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic (Abu Farha et al., 2021). We tackled both subtasks, namely Sarcasm Detection (Subtask 1) and Sentiment Analysis (Subtask 2). We used state-of-the-ar
t pretrained contextualized text representation models and fine-tuned them according to the downstream task in hand. As a first approach, we used Google's multilingual BERT and then other Arabic variants: AraBERT, ARBERT and MARBERT. The results found show that MARBERT outperforms all of the previously mentioned models overall, either on Subtask 1 or Subtask 2.
We present three methods developed for the Shared Task on Sarcasm and Sentiment Detection in Arabic. We present a baseline that uses character n-gram features. We also propose two more sophisticated methods: a recurrent neural network with a word lev
el representation and an ensemble classifier relying on word and character-level features. We chose to present results from an ensemble classifier but it was not very successful as compared to the best systems : 22th/37 on sarcasm detection and 15th/22 on sentiment detection. It finally appeared that our baseline could have been improved and beat those results.
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.
The prominence of figurative language devices, such as sarcasm and irony, poses serious challenges for Arabic Sentiment Analysis (SA). While previous research works tackle SA and sarcasm detection separately, this paper introduces an end-to-end deep
Multi-Task Learning (MTL) model, allowing knowledge interaction between the two tasks. Our MTL model's architecture consists of a Bidirectional Encoder Representation from Transformers (BERT) model, a multi-task attention interaction module, and two task classifiers. The overall obtained results show that our proposed model outperforms its single-task and MTL counterparts on both sarcasm and sentiment detection subtasks.
Sarcasm detection is one of the top challenging tasks in text classification, particularly for informal Arabic with high syntactic and semantic ambiguity. We propose two systems that harness knowledge from multiple tasks to improve the performance of
the classifier. This paper presents the systems used in our participation to the two sub-tasks of the Sixth Arabic Natural Language Processing Workshop (WANLP); Sarcasm Detection and Sentiment Analysis. Our methodology is driven by the hypothesis that tweets with negative sentiment and tweets with sarcasm content are more likely to have offensive content, thus, fine-tuning the classification model using large corpus of offensive language, supports the learning process of the model to effectively detect sentiment and sarcasm contents. Results demonstrate the effectiveness of our approach for sarcasm detection task over sentiment analysis task.
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