Universal Post-Processing Networks for Joint Optimization of Modules in Task-Oriented Dialogue Systems
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916594750324736 |
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| author | Ohashi, Atsumoto Higashinaka, Ryuichiro |
| author_facet | Ohashi, Atsumoto Higashinaka, Ryuichiro |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_00747 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Universal Post-Processing Networks for Joint Optimization of Modules in Task-Oriented Dialogue Systems Ohashi, Atsumoto Higashinaka, Ryuichiro Computation and Language Artificial Intelligence 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. |
| title | Universal Post-Processing Networks for Joint Optimization of Modules in Task-Oriented Dialogue Systems |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2502.00747 |