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Register a new userOverview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic
نظرة عامة على المهمة المشتركة WANLP 2021 بشأن السخرية والكشف عن المعنويات باللغة العربية
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توفر هذه الورقة نظرة عامة على المهمة المشتركة WANLP 2021 بشأن السخرية والكشف عن المعنويات باللغة العربية.المهمة المشتركة لها مفتاحان فرعي: الكشف عن السخرية (الفرعية 1) وتحليل المعرفات (SubTask 2).تهدف هذه المهمة المشتركة إلى الترويج والاهتمام بالكشف عن السخرية العربية، وهو أمر بالغ الأهمية لتحسين الأداء في مهام أخرى مثل تحليل المعرفات.تتكون DataSet المستخدمة في هذه المهمة المشتركة، وهي Arsarcasm-V2، من 15،548 تغريدات تسمى السخرية والشعور واللهجة.تلقينا 27 و 22 عروضا للمجموعات الفرعية 1 و 2 على التوالي.تعتمد معظم النهج على استخدام النماذج اللغوية المدربة مسبقا وضبطها جيدا مثل أرابيرت وماربرت.وكانت أفضل النتائج التي تحققت في مهام تحليل السخرية وتحليل المعنويات 0.6225 F1 و 0.748 F1-PN على التوالي.
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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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.
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.
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 w
e 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.
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.
We present an overview of the SCIVER shared task, presented at the 2nd Scholarly Document Processing (SDP) workshop at NAACL 2021. In this shared task, systems were provided a scientific claim and a corpus of research abstracts, and asked to identify
which articles Support or Refute the claim as well as provide evidentiary sentences justifying those labels. 11 teams made a total of 14 submissions to the shared task leaderboard, leading to an improvement of more than +23 F1 on the primary task evaluation metric. In addition to surveying the participating systems, we provide several insights into modeling approaches to support continued progress and future research on the important and challenging task of scientific claim verification.
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