  From the earliest experiments in the 20th century to the utilization of large
language models and transformers, dialogue systems research has continued to
evolve, playing crucial roles in numerous fields. This paper offers a
comprehensive review of these systems, tracing their historical development and
examining their fundamental operations. We analyze popular and emerging
datasets for training and survey key contributions in dialogue systems
research, including traditional systems and advanced machine learning methods.
Finally, we consider conventional and transformer-based evaluation metrics,
followed by a short discussion of prevailing challenges and future prospects in
the field.
