Enhancing End-to-End Multi-Task Dialogue Systems: A Study on Intrinsic Motivation Reinforcement Learning Algorithms for Improved Training and Adaptability

📄 arXiv: 2401.18040v2 📥 PDF

作者: Navin Kamuni, Hardik Shah, Sathishkumar Chintala, Naveen Kunchakuri, Sujatha Alla Old Dominion

分类: cs.CL, cs.AI

发布日期: 2024-01-31 (更新: 2024-03-25)

备注: 6 pages, 1 figure, 18th IEEE International Conference on Semantic Computing

💡 一句话要点

提出内在动机强化学习算法以提升多任务对话系统的训练与适应性

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)

关键词: 多任务对话系统 内在动机 强化学习 随机网络蒸馏 语义相似性 训练效率 适应性

📋 核心要点

  1. 现有的多任务对话系统在奖励信号上过于简单，限制了系统的学习和适应能力。
  2. 本研究通过内在动机强化学习算法，利用随机网络蒸馏和语义相似性来提升对话系统的训练效果。
  3. 实验结果表明，采用内在动机的系统在成功率和其他性能指标上均显著优于传统方法。

📝 摘要（中文）

端到端的多任务对话系统通常设计为具有独立模块的对话管道，其中策略模块对于响应用户输入至关重要。当前的对话系统仅提供简单的奖励信号，限制了其学习能力。本研究旨在探讨内在动机强化学习算法，通过建立内部激励系统，加速训练并提高行动质量评估能力。我们采用随机网络蒸馏和基于好奇心的强化学习技术，利用语义相似性促进探索。实验结果显示，基于内在动机的对话系统在MultiWOZ数据集上表现优于依赖外部激励的策略，成功率达到73%，显著高于基线的60%。此外，预订率和完成率较基线提升了10%。

🔬 方法详解

问题定义：本论文旨在解决当前多任务对话系统中奖励信号简单、学习能力不足的问题。现有方法依赖外部激励，导致系统在复杂环境中的适应性差。

核心思路：论文提出通过内在动机强化学习算法，建立一个内部激励系统，使得代理能够更快地学习并评估其行动质量，从而提升对话系统的整体性能。

技术框架：整体架构包括多个模块，主要包括环境模块、策略模块和奖励模块。环境模块提供用户输入，策略模块根据输入生成响应，而奖励模块则根据内在动机和外部反馈来调整策略。

关键创新：最重要的技术创新在于引入随机网络蒸馏和基于好奇心的强化学习，这些方法通过语义相似性来促进状态访问频率的提升，鼓励系统探索更多的对话策略。

关键设计：在参数设置上，采用了特定的损失函数来平衡内在和外在奖励，同时在网络结构上引入了随机网络蒸馏技术，以提高对话系统的学习效率和适应性。

📊 实验亮点

实验结果显示，基于内在动机的对话系统在MultiWOZ数据集上的成功率达到73%，相比基线的60%有显著提升。此外，预订率和完成率较基线提高了10%，表明该方法在实际应用中的有效性和优势。

🎯 应用场景

该研究的潜在应用领域包括智能客服、虚拟助手和人机交互等场景。通过提升对话系统的训练效率和适应能力，可以更好地满足用户需求，提供个性化服务，未来可能在多领域的对话系统中得到广泛应用。

📄 摘要（原文）

End-to-end multi-task dialogue systems are usually designed with separate modules for the dialogue pipeline. Among these, the policy module is essential for deciding what to do in response to user input. This policy is trained by reinforcement learning algorithms by taking advantage of an environment in which an agent receives feedback in the form of a reward signal. The current dialogue systems, however, only provide meagre and simplistic rewards. Investigating intrinsic motivation reinforcement learning algorithms is the goal of this study. Through this, the agent can quickly accelerate training and improve its capacity to judge the quality of its actions by teaching it an internal incentive system. In particular, we adapt techniques for random network distillation and curiosity-driven reinforcement learning to measure the frequency of state visits and encourage exploration by using semantic similarity between utterances. Experimental results on MultiWOZ, a heterogeneous dataset, show that intrinsic motivation-based debate systems outperform policies that depend on extrinsic incentives. By adopting random network distillation, for example, which is trained using semantic similarity between user-system dialogues, an astounding average success rate of 73% is achieved. This is a significant improvement over the baseline Proximal Policy Optimization (PPO), which has an average success rate of 60%. In addition, performance indicators such as booking rates and completion rates show a 10% rise over the baseline. Furthermore, these intrinsic incentive models help improve the system's policy's resilience in an increasing amount of domains. This implies that they could be useful in scaling up to settings that cover a wider range of domains.