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About this paper
Jinjie Ni, Tom Young, Vlad Pandelea, Fuzhao Xue, Erik Cambria
Researchers from Nanyang Technological University provide a systematic survey of recent advancements in deep learning-based dialogue systems, meticulously categorizing approaches by model and system types. This work consolidates a vast and complex body of literature, highlighting key challenges and projecting future research trends.
Key insights
- Deep learning has become the dominant paradigm, underpinning most state-of-the-art dialogue systems due to its superior representation learning capabilities.
- Task-Oriented Dialogue (TOD) and Open-Domain Dialogue (ODD) systems, while both leveraging deep learning, present distinct architectural challenges and require different approaches to context management, response generation, and evaluation.
- Persistent challenges include improving response coherence, diversity, speaker consistency, empathetic generation, efficient dialogue state tracking, and effective integration of external knowledge.
Summary by alphaXiv.
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