Classical Attention Analysis in Neural Networks

Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. This survey provides a structured and comprehensive overview of the developments in modeling attention. In particular, we propose a taxonomy that groups existing techniques into coherent categories. We review salient neural architectures in which attention has been incorporated and discuss applications in which modeling attention has shown a significant impact. We also describe how attention has been used to improve the interpretability of neural networks. Finally, we discuss some future research directions in attention. We hope this survey will provide a succinct introduction to attention models and guide practitioners while developing approaches for their applications.

In humans, Attention is a core property of all perceptual and cognitive operations. Given our limited ability to process competing sources, attention mechanisms select, modulate, and focus on the information most relevant to behavior. For decades, concepts and functions of attention have been studied in philosophy, psychology, neuroscience, and computing. For the last 6 years, this property has been widely explored in deep neural networks. Currently, the state-of-the-art in Deep Learning is represented by neural attention models in several application domains. This survey provides a comprehensive overview and analysis of developments in neural attention models. We systematically reviewed hundreds of architectures in the area, identifying and discussing those in which attention has shown a significant impact. We also developed and made public an automated methodology to facilitate the development of reviews in the area. By critically analyzing 650 works, we describe the primary uses of attention in convolutional, recurrent networks, and generative models, identifying common subgroups of uses and applications. Furthermore, we describe the impact of attention in different application domains and their impact on neural networks’ interpretability. Finally, we list possible trends and opportunities for further research, hoping that this review will provide a succinct overview of the main attentional models in the area and guide researchers in developing future approaches that will drive further improvements.

Sentiment analysis, the intricate task of discerning and classifying the myriad of sentiments conveyed within textual data, has captured substantial interest and intrigue, primarily driven by the pervasive utilization and influence of social media platforms. In this study, a novel approach to enhance sentiment analysis of Twitter data by employing Recurrent Neural Networks (RNNs) with an attention mechanism is proposed. The proposed model leverages the sequential nature of tweets and the attention mechanism to capture the inherent dependencies between words and highlight salient information. The RNN-based model on a large-scale dataset of annotated Twitter data, encompassing diverse sentiments is trained. The model effectively learns the contextual information and sentiment patterns, enabling accurate sentiment classification. A comprehensive set of tests were run to evaluate the effectiveness of this methodology, and the outcomes were meticulously compared to those of traditional machine learning algorithms and established deep learning models. The empirical findings demonstrate that proposed attention-based RNN model performs better than competing methods and achieves cutting-edge performance on sentiment analysis of Twitter data. Moreover, an in-depth analysis of the attention weights generated by the model, shedding light on the significant words and phrases influencing sentiment classification is conducted. This provides valuable insights into the underlying sentiment dynamics in Twitter data. Proposed research contributes to the field of sentiment analysis by proposing an effective and robust approach for Twitter sentiment classification. The findings highlight the potential of RNNs with attention mechanisms in capturing the nuanced sentiment expressions prevalent in social media text. The proposed model can facilitate various applications, including real-time sentiment monitoring, brand reputation analysis, and public opinion tracking, benefiting industries and researchers alike.

Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).