面向任务型对话系统中模块联合优化的通用后处理网络
Universal Post-Processing Networks for Joint Optimization of Modules in Task-Oriented Dialogue Systems
AI总结：
提出通用后处理网络（UniPPNs）联合优化任务型对话系统所有模块输出，采用模块级马尔可夫决策过程的强化学习算法稳定联合学习，在MultiWOZ数据集上证明其任务完成能力优于传统PPNs。
摘要：
后处理网络（PPNs）是修改任务型对话系统中任意模块输出的组件，通过强化学习（RL）进行优化以提升系统整体任务完成能力。然而，以往基于PPN的方法仅限于处理系统中的部分模块，这在提升系统性能方面存在显著局限。本研究提出了一种联合优化方法，使用通用后处理网络（UniPPNs）对所有模块的输出进行后处理。UniPPNs是基于语言模型的网络，能将修改任意模块输出作为序列转换任务来执行。此外，我们的RL算法采用模块级马尔可夫决策过程，能对每个模块进行细粒度的价值与优势估计，从而稳定所有模块输出的联合学习后处理。通过使用MultiWOZ数据集的模拟与人工评估实验，我们证明UniPPN在任务型对话系统的任务完成能力上优于传统PPNs。
英文摘要：
Post-processing networks (PPNs) are components that modify the outputs of arbitrary modules in task-oriented dialogue systems and are optimized using reinforcement learning (RL) to improve the overall task completion capability of the system. However, previous PPN-based approaches have been limited to handling only a subset of modules within a system, which poses a significant limitation in improving the system performance. In this study, we propose a joint optimization method for post-processing the outputs of all modules using universal post-processing networks (UniPPNs), which are language-model-based networks that can modify the outputs of arbitrary modules in a system as a sequence-transformation task. Moreover, our RL algorithm, which employs a module-level Markov decision process, enables fine-grained value and advantage estimation for each module, thereby stabilizing joint learning for post-processing the outputs of all modules. Through both simulation-based and human evaluation experiments using the MultiWOZ dataset, we demonstrated that UniPPN outperforms conventional PPNs in the task completion capability of task-oriented dialogue systems.
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