A Transformer-based approach to Irony and Sarcasm detection (1911.10401v2)

Abstract: Figurative Language (FL) seems ubiquitous in all social-media discussion forums and chats, posing extra challenges to sentiment analysis endeavors. Identification of FL schemas in short texts remains largely an unresolved issue in the broader field of NLP, mainly due to their contradictory and metaphorical meaning content. The main FL expression forms are sarcasm, irony and metaphor. In the present paper we employ advanced Deep Learning (DL) methodologies to tackle the problem of identifying the aforementioned FL forms. Significantly extending our previous work [71], we propose a neural network methodology that builds on a recently proposed pre-trained transformer-based network architecture which, is further enhanced with the employment and devise of a recurrent convolutional neural network (RCNN). With this set-up, data preprocessing is kept in minimum. The performance of the devised hybrid neural architecture is tested on four benchmark datasets, and contrasted with other relevant state of the art methodologies and systems. Results demonstrate that the proposed methodology achieves state of the art performance under all benchmark datasets, outperforming, even by a large margin, all other methodologies and published studies.

• The paper introduces a hybrid RCNN-RoBERTa model that leverages a Transformer architecture combined with recurrent and convolutional layers for improved irony and sarcasm detection in text.
• This methodology effectively extracts semantic and contextual features from text, reducing reliance on extensive feature engineering or large lexicon dictionaries.
• Experimental results on benchmark datasets demonstrate that the proposed RCNN-RoBERTa model outperforms other state-of-the-art models like BERT and XLNet in figurative language detection tasks, showing promise for advanced sentiment analysis applications.

Overview of Transformer-Based Approach for Irony and Sarcasm Detection

The paper "A Transformer-based Approach to Irony and Sarcasm Detection" explores the intricacies of NLP regarding figurative language detection, particularly focusing on irony and sarcasm. This is a topic of increasing importance due to the proliferation of social media where users commonly employ figurative expressions that pose challenges to sentiment analysis owing to their metaphorical and often contradictory nature. The authors introduce a sophisticated machine learning methodology leveraging deep learning and Transformer architectures to effectively address this complexity in language detection.

Methodology

The research builds upon established deep learning frameworks, specifically pre-trained Transformer networks, which are a subset of attention-based models demonstrating superior performance in various NLP tasks. The proposed model integrates a RoBERTa-based Transformer to extract rich semantic embeddings, complemented by a Recurrent Convolutional Neural Network (RCNN) to capture contextual dependencies that are crucial for discerning figurative language. This hybrid approach mitigates common issues found in machine learning tasks that rely heavily on engineered features and prolonged preprocessing, such as the need for large lexicon dictionaries or exhaustive text cleaning processes.

Experimental Validation

The authors conduct thorough experiments using benchmark datasets from well-recognized sources such as SemEval and Reddit. These datasets include various social media excerpts rich with figurative language instances. The performance metrics used incorporate accuracy, precision, recall, F1-score, and area under the ROC curve, providing a comprehensive evaluation of the model's efficacy. The results demonstrate that the RCNN-RoBERTa architecture outperforms other state-of-the-art models, including BERT and XLNet, in irony and sarcasm detection tasks.

Implications for NLP and AI

This research presents significant contributions to the field of NLP, especially in the context of sentiment analysis. The development of a robust model capable of effectively parsing figurative language opens pathways for implementing advanced sentiment analysis algorithms in consumer insight applications, public opinion monitoring, and content recommendation systems. From a theoretical standpoint, the integration of recurrent and convolutional layers within a Transformer framework illustrates an innovative approach to enhancing context-awareness in LLMs.

Future Prospects

The findings of this paper suggest that ongoing explorations into hybrid deep learning structures may continuously improve NLP systems' adaptability to nuanced language phenomena. Future developments could include tuning the architectures to cater specifically to other complex tasks, such as metaphor detection, or expanding the models to support multilingual analysis, thereby further broadening their application scope.

In summary, the paper represents a methodical and sound advancement in AI's ability to process and interpret human language nuances, showcasing the evolving capabilities of deep learning methodologies to address sophisticated linguistic challenges.