Lifelong and Continual Learning Dialogue Systems

TL;DR

The work addresses the limitations of static, data-heavy dialogue systems by proposing a unified framework for open-world continual learning in dialogue: SOLA (Self-initiated Open-world continual Learning and Adaptation). It introduces LINC and SOLA's eight-subsystem architecture to enable autonomous, on-the-job learning, novelty detection, and automatic data acquisition from user interactions and external sources. A concrete SOLA-based dialogue system (CML) illustrates how novelty is detected, new tasks are created, and learning occurs incrementally without engineer-led retraining, with comparisons to novelty detection, open-world learning, and continual learning. The study highlights open challenges and outlines a roadmap toward truly autonomous, adaptable conversational agents capable of learning from open environments and evolving user needs.

Abstract

Dialogue systems, commonly known as chatbots, have gained escalating popularity in recent times due to their wide-spread applications in carrying out chit-chat conversations with users and task-oriented dialogues to accomplish various user tasks. Existing chatbots are usually trained from pre-collected and manually-labeled data and/or written with handcrafted rules. Many also use manually-compiled knowledge bases (KBs). Their ability to understand natural language is still limited, and they tend to produce many errors resulting in poor user satisfaction. Typically, they need to be constantly improved by engineers with more labeled data and more manually compiled knowledge. This book introduces the new paradigm of lifelong learning dialogue systems to endow chatbots the ability to learn continually by themselves through their own self-initiated interactions with their users and working environments to improve themselves. As the systems chat more and more with users or learn more and more from external sources, they become more and more knowledgeable and better and better at conversing. The book presents the latest developments and techniques for building such continual learning dialogue systems that continuously learn new language expressions and lexical and factual knowledge during conversation from users and off conversation from external sources, acquire new training examples during conversation, and learn conversational skills. Apart from these general topics, existing works on continual learning of some specific aspects of dialogue systems are also surveyed. The book concludes with a discussion of open challenges for future research.

Figures (4)

• Figure 1: A typical task-oriented dialogue system with its modules.
• Figure 3: Architecture of the classical machine learning paradigm, where $T$ is the task and $D$ is its training data. The links in blue reflect the learning process and the links in black reflect the application process of the learned model.
• Figure 4: Architecture of a traditional lifelong learning framework. $\mathcal{T}_1, ..., \mathcal{T}_N$ are the previously learned tasks, $\mathcal{T}_{N+1}$ is the current new task to be learned and $D_{N+1}$ is its training data. The C-Learner (Continual Learner) learns by leveraging the relevant prior knowledge identified by the Task-based Knowledge Miner from the Knowledge Base (KB), which contains the retained knowledge in the past. It also deals with the catastrophic forgetting.
• Figure 5: Architecture of the primary task performer or any supporting function. OWC-Learner means Open-World Continual Learner.