With the progressive evolution of the Internet and social media, there is a rapid rise in online data. As the volume of data grows, there is a necessity to handle and extract relevant information from the massive knowledge base is getting more difficult. Such challenges and difficulties are overcome by using Natural language processing (NLP) techniques.
   Recently, the advent of deep learning techniques essentially altered the landscapes in the areas that include robotics, artificial intelligence, speech recognition, computer vision, game playing, natural language generation, to name a few. Notably, the striking achievement of deep learning in an extensive range of NLP applications has functioned as a benchmark for advancing the significant task of artificial intelligence. It attempts to characterize and understand the text based on the arbitrary notions of content, morphemes, clauses, and other components of the linguistic structure to reveal the encoded content in the human language. As the capability of representational learning of complex tasks, the deep learning models outperform in natural language processing for the opinion mining, whereas the sentiment among reviews and posts are identified to estimate the customer opinion. Furthermore, it is widely applied in automatic summarization, question answering systems, text classification, language modeling, information retrieval, spam filter, machine translation, speech recognition.
   There is a massive amount of knowledge sources for the NLP like linguistic knowledge (e.g., Lexicon, grammar), world knowledge (e.g., Wikipedia), and symbols. The deep learning model in the NLP tasks has not yet made any significant progress in the efficient utilization of knowledge. Also, symbol representation using the deep learning methods eases interpretation and manipulation. At the same time, the vector representations are more robust to Noisy and Ambiguous data. However, taking advantage of both the symbol and vector representation data is still a bottleneck for natural language processing. Deep learning requires an external force when confronting the complex natural language processing task.
   The deep learning methods are significantly out-compete the other methods on several challenging natural language constraints based on the simple and singular models. Despite its lack of interpretation, the theoretical foundation entails a potent computing resource and a massive amount of training data. It demands substantial training data to learn the regularity of the specific domain. Furthermore, it is more vulnerable to overfit the data when the availability of training data becomes smaller. It often suffers the issues owing to the lack of inference. Also, the deep learning model based on natural language processing meets several challenges when dealing with the context-sensitive nature of the text.

Proposal Topic Ideas:
  •  Instigating dynamic neural networks in NLP is an emerging research scope that proffers dynamically adjusting computational paths conducive to scaling up neural networks with sub-linear increases in computation and time; it also provides better outcomes for pre-trained language models by permitting model pre-training with trillions of parameters and faster inference on mobile devices.
  •  Federated learning encounters deep learning, satisfies privacy concern of text data originating from different end-users, and provide research accustomed to bench-marking framework due to its applicability ranges from language modeling to health text mining.
  •  Graph neural network (GNN) for many NLP tasks is novel furtherance, facilitates NLP problems in the best way using a graph structure. Various NLP problems are solved using GNN with proper graph representation learning to learn unique characteristics of different graph-structures data and effective complex data modeling.
  •  Psychometric NLP is the new research area that measures psychometric dimensions for understanding user behaviors in various contexts, including health, security, e-commerce, and finance. Deep learning architecture for psychometric NLP comprises the representation embedding, demographic embedding, and structural equation model encoder to represent rich and diverse psychometric information effectively.
  •  Utilizing the attention mechanism in NLP is a recent development and extremely useful for high-level representation as it increases the accuracy and works well on long sentences. The attention mechanism is ubiquitously applied for NLP tasks such as sequence labeling, text classification, reasoning, and generative tasks.
  •  Several advancements in deep learning-based NLP tasks have been investigated in the chore of syntax, semantics, discourse, and speech with different approaches and state-of-the-art models related to deep learning.