The recent developments in the field of natural language processing (NLP) have shown a significant shift towards leveraging contextual information and advanced neural network architectures for improved performance in various tasks. One notable trend is the integration of contextual awareness into intent classification systems, which traditionally relied solely on the current utterance. This approach enhances the accuracy of intent prediction by considering dialogue history, thereby improving the overall user experience in conversational systems. Additionally, the use of adversarial training in conjunction with pre-trained language models has shown promising results in text classification tasks, particularly in specialized domains such as telecom fraud detection. This method not only improves classification accuracy but also enhances the robustness of the model against adversarial attacks. Another area witnessing innovation is the application of transformer-based models for grammatical error detection in under-resourced languages like Bangla. These models, combined with rule-based post-processing, offer a robust solution for automated grammar checking, which is crucial for developing language-specific typing assistants. Overall, the field is progressing towards more context-aware, robust, and language-inclusive models, driven by the need for more accurate and efficient NLP solutions.