This research, titled Enhancing Conversational Agents with Advanced Natural Language Processing Techniques for Dynamic Question Answering, explores the development of DynaQA-1, a novel model designed to improve the adaptability and contextual relevance of conversational agents (CAs). Despite progress in AI-driven conversational systems, many existing CAs remain limited by static response generation, often producing contextually irrelevant or repetitive answers.

DynaQA-1 addresses these challenges by integrating a Sequence-to-Sequence (Seq2Seq) architecture that combines BERT (Bidirectional Encoder Representations from Transformers) as the encoder and GPT-2 (Generative Pretrained Transformer) as the decoder. Advanced techniques such as intent recognition, dialogue state tracking, dialogue act recognition, and coreference resolution are incorporated to enhance context awareness and response adaptability.

The model's performance was evaluated on benchmark datasets, including Snips, MultiWOZ, and CoNLL-2012, demonstrating notable improvements in accuracy, precision, recall, and F1 scores compared to traditional, baseline, and state-of-the-art models. These results affirm DynaQA-1 as a significant advancement in conversational AI, paving the way for more sophisticated and dynamic CAs. However, challenges related to computational resources and broader NLP integration were also identified, highlighting areas for further exploration.

As this research is pending publication in a journal, the results and source code associated with DynaQA-1 cannot be disclosed publicly until the publication process is complete. This restriction ensures the originality and integrity of the work are preserved for its debut in the academic domain.