Advancements in Deep Learning Architectures for Natural Language Processing Tasks

Recent years have been an active testing ground for artificial neural networks for language understanding, a very important aspect of NLP. In this respect, emerging NLP technologies are largely motivated by the rising requirements to cope with the issues raised by different NLP tasks, allowing the processing and analysis of large text data samples, uncovering complex language behaviors, as well as extracting valuable information from disorganized text. NLP (Natural Language Processing) has proven to be the most successful field of machine learning thanks to its capability to teach itself and detect all kinds of features on its own based on enormous amounts of data. In NLP tasks like language modelling, text classification, emotion analysis, and machine translation, RNNs, CNNs, and transformer-based models have been used in new ways. While NLP is generally agreed upon the difficulties it faces, the progress of technology also gives birth to unexpected challenges. Thus, two factors, namely the expanding collections of large text datasets and the pressing need for more accurate and time-saving NLP models that emerge as a consequence are giving rise to new kinds of deep learning models and techniques. Here, this paper analyzes as a whole the most recent achievement of neural architectures for natural language processing applications. From introducing current models and approaches in NLP, highlighting their strengths and weaknesses, and identifying the areas to be researched in the future, this paper will conduct this discussion. Then, this paper will go on and investigate the of one in NLP, together with the importance of constantly improving architectures which are responsible for tackling these hard tasks. Subsequently, it will talk about the recent breakthroughs in deep learning models namely RNNs, CNNs, transformer-based models and attention mechanisms will be discussed next. At last, this paper will cover the ever-evolving roofline in NLP research, including transfer learning, self-supervised learning, and multimodal learning. Moreover, this paper will also underline the current shortcomings of existing NLP models and locate the themes where research needs to be reevaluated. This article, through the deep learning architecture review for NLP, offered a full-range overview of the recent advancement in deep learning, and this article is developed as a valuable corpus for the researcher, practitioners, and students in the field of NLP.