让任务型对话机器人自学习，提升适应性与准确性

Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality

• 提出自学习机制，使机器人在无干预下持续优化
• 实现在动态环境中的准确任务执行，降低人工依赖
• 适合需要长期运维的智能客服系统研发者

在对话研究中，开发无需或极少人工干预、具备良好适应性、可扩展性和准确性的任务型机器人是一项重大挑战。本文探讨了构建此类机器人的障碍与潜在解决方案，重点研究了创新技术，使机器人能够在不断变化的环境中实现自主学习与适应。

原文摘要 · Abstract (English)

Developing adaptable, extensible, and accurate task bots with minimal or zero human intervention is a significant challenge in dialog research. This thesis examines the obstacles and potential solutions for creating such bots, focusing on innovative techniques that enable bots to learn and adapt autonomously in constantly changing environments.

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