What Does Your Smile Mean? Jointly Detecting Multi-Modal Sarcasm and Sentiment Using Quantum Probability

Sarcasm and sentiment embody intrinsic uncertainty of human cognition, making joint detection of multi-modal sarcasm and sentiment a challenging task. In view of the advantages of quantum probability (QP) in modeling such uncertainty, this paper explores the potential of QP as a mathematical framework and proposes a QP driven multi-task (QPM) learning framework. The QPM framework involves a complex-valued multi-modal representation encoder, a quantum-like fusion subnetwork and a quantum measurement mechanism. Each multi-modal (e.g., textual, visual) utterance is first encoded as a quantum superposition of a set of basis terms using a complex-valued representation. Then, the quantum-like fusion subnetwork leverages quantum state composition and quantum interference to model the contextual interaction between adjacent utterances and the correlations across modalities respectively. Finally, quantum incompatible measurements are performed on the multi-modal representation of each utterance to yield the probabilistic outcomes of sarcasm and sentiment recognition. The experimental results show that our model achieves a state-of-the-art performance.

Perceived and Intended Sarcasm Detection with Graph Attention Networks

Existing sarcasm detection systems focus on exploiting linguistic markers, context, or user-level priors. However, social studies suggest that the relationship between the author and the audience can be equally relevant for the sarcasm usage and interpretation. In this work, we propose a framework jointly leveraging (1) a user context from their historical tweets together with (2) the social information from a user's conversational neighborhood in an interaction graph, to contextualize the interpretation of the post. We use graph attention networks (GAT) over users and tweets in a conversation thread, combined with dense user history representations. Apart from achieving state-of-the-art results on the recently published dataset of 19k Twitter users with 30K labeled tweets, adding 10M unlabeled tweets as context, our results indicate that the model contributes to interpreting the sarcastic intentions of an author more than to predicting the sarcasm perception by others.

A Contextual Word Embedding for Arabic Sarcasm Detection with Random Forests

Sarcasm detection is of great importance in understanding people's true sentiments and opinions. Many online feedbacks, reviews, social media comments, etc. are sarcastic. Several researches have already been done in this field, but most researchers studied the English sarcasm analysis compared to the researches are done in Arabic sarcasm analysis because of the Arabic language challenges. In this paper, we propose a new approach for improving Arabic sarcasm detection. Our approach is using data augmentation, contextual word embedding and random forests model to get the best results. Our accuracy in the shared task on sarcasm and sentiment detection in Arabic was 0.5189 for F1-sarcastic as the official metric using the shared dataset ArSarcasmV2 (Abu Farha, et al., 2021).

Explainable Detection of Sarcasm in Social Media

Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions makes sarcasm detection very difficult. In this work, we focus on detecting sarcasm in textual conversations, written in English, from various social networking platforms and online media. To this end, we develop an interpretable deep learning model using multi-head self-attention and gated recurrent units. We show the effectiveness and interpretability of our approach by achieving state-of-the-art results on datasets from social networking platforms, online discussion forums, and political dialogues.

iCompass at Shared Task on Sarcasm and Sentiment Detection in Arabic

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-art 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.

Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic

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.

SarcasmDet at Sarcasm Detection Task 2021 in Arabic using AraBERT Pretrained Model

This paper presents one of the top five winning solutions for the Shared Task on Sarcasm and Sentiment Detection in Arabic (Subtask-1 Sarcasm Detection). The goal of the task is to identify whether a tweet is sarcastic or not. Our solution has been developed using ensemble technique with AraBERT pre-trained model. We describe the architecture of the submitted solution in the shared task. We also provide the experiments and the hyperparameter tuning that lead to this result. Besides, we discuss and analyze the results by comparing all the models that we trained or tested to achieve a better score in a table design. Our model is ranked fifth out of 27 teams with an F1 score of 0.5985. It is worth mentioning that our model achieved the highest accuracy score of 0.7830

Humor and irony in the tale on the tongue of animals at Lafontaine and Ahmed Shawki

The goal of the poets Lafontaine and Shawki to employ ridicule and humor in their tales on the tongue of animals, is to create moral values related to the spirit of the times and events, and provide the sermon and the lesson resulting from experience and depth of suffering, and enjoy the reader and entertain himself.