Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey (2105.04387v5)

Abstract: Dialogue systems are a popular NLP task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving study are involved. As a result, a multitude of novel works on this task are carried out, and most of them are deep learning based due to the outstanding performance. In this survey, we mainly focus on the deep learning based dialogue systems. We comprehensively review state-of-the-art research outcomes in dialogue systems and analyze them from two angles: model type and system type. Specifically, from the angle of model type, we discuss the principles, characteristics, and applications of different models that are widely used in dialogue systems. This will help researchers acquaint these models and see how they are applied in state-of-the-art frameworks, which is rather helpful when designing a new dialogue system. From the angle of system type, we discuss task-oriented and open-domain dialogue systems as two streams of research, providing insight into the hot topics related. Furthermore, we comprehensively review the evaluation methods and datasets for dialogue systems to pave the way for future research. Finally, some possible research trends are identified based on the recent research outcomes. To the best of our knowledge, this survey is the most comprehensive and up-to-date one at present for deep learning based dialogue systems, extensively covering the popular techniques. We speculate that this work is a good starting point for academics who are new to the dialogue systems or those who want to quickly grasp up-to-date techniques in this area.

Advances in Deep Learning-Based Dialogue Systems: A Comprehensive Survey

The paper "Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey" offers an extensive overview of the state-of-the-art developments in dialogue systems focused on deep learning models. It addresses a crucial area of NLP by chronicling both model types and system types that underline modern dialogue frameworks, particularly those driven by deep learning, which have demonstrated notable performance gains.

Overview and Classification

The survey categorically splits dialogue systems into two major types based on application: task-oriented dialogue systems (TOD) and open-domain dialogue systems (OOD). The paper provides a detailed review from both the model perspective and the system perspective, showcasing the principles, characteristics, and applications of different architectures employed within these classifications. From task completion to open-ended conversational tasks, the survey explores how each system type interacts with and benefits from deep learning advancements.

Model Types and Architectures

Significant attention is paid to the architectures invigorating current dialogue systems. The paper discusses:

• Convolutional Neural Networks (CNNs): Leveraged for text feature extraction but rarely as the primary encoders due to their limitations in sequence point processing.
• Recurrent Neural Networks (RNNs) and Variants: Including LSTMs, GRUs, and bidirectional RNNs, utilized for their proficiency in handling sequential data, a staple in dialogue systems.
• Sequence-to-Sequence Models: A crucial architecture for generative tasks with discussions on attention mechanisms and Transformer models, which have reshaped the landscape of sequential modeling.
• Memory Networks and Copy Mechanisms: Addressing the need for systems to access and utilize external knowledge bases or past interactions.
• Advanced Techniques: Such as Deep Reinforcement Learning and Generative Adversarial Networks (GANs) that bring dynamism and adaptability to system responses.

System Types and Evaluation

The survey articulates the nuance between modular approaches and holistic end-to-end training in task-oriented systems, highlighting the former's methodological clarity and fine-tuned control versus the latter’s inherent flexibility and scalable adaptability. It also addresses the challenge of evaluating dialogue systems, noting the divergence between task-oriented system metrics and those suitable for open-domain systems.

Research Challenges and Future Directions

A key component of the paper lies in identifying persistent challenges and open questions in dialogue system development. For task-oriented systems, issues like dialogue state tracking efficiency, policy learning robustness, and cross-domain adaptability are underscored. In open-domain dialogue systems, challenges revolve around maintaining contextual relevance and producing responses that are both diverse and coherent.

The paper speculates on future paths this field may take, suggesting that multimodal dialogue systems, enhanced by integrative learning across modalities, and evolving user modeling techniques could drive new advancements. Additionally, adapting systems through few-shot learning and leveraging vast unstructured data from the internet represent foundational elements for next-generation conversational AI.

Conclusion

This survey stands as a valuable resource for researchers, mapping the landscape of current methodologies and technologies underpinning dialogue systems. By synthesizing a wealth of research, it not only provides a benchmark of existing capabilities but also serves as a springboard for future exploration and innovation in dialogue system technology. This comprehensive coverage is indispensable for those looking to understand the intricacies of dialogue systems or seeking to contribute to this rapidly advancing field.