KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords Learning

• URL: http://arxiv.org/abs/2211.16773v5
• Date: Thu, 19 Oct 2023 19:16:12 GMT
• Title: KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords Learning
• Authors: Xiao Yu, Qingyang Wu, Kun Qian, Zhou Yu
• Abstract summary: In task-oriented dialogs (TOD), reinforcement learning algorithms train a model to directly optimize response for task-related metrics. We investigate an approach to create a more efficient RL-based algorithm to improve TOD performance in an offline setting. Experiments on the MultiWoZ dataset show our new training algorithm, Keywords Reinforcement Learning with Next-word Sampling (KRLS), achieves state-of-the-art performance.
• Score: 25.421649004269373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In task-oriented dialogs (TOD), reinforcement learning (RL) algorithms train a model to directly optimize response for task-related metrics. However, RL needs to perform exploration, which can be time-consuming due to the slow auto-regressive sequence generation process. We investigate an approach to create a more efficient RL-based algorithm to improve TOD performance in an offline setting. First, we use a faster generation procedure that samples from independent next-word distributions after training the language model (LM) with supervised learning. We then introduce a fine-grained reward function to help the model focus on learning key information in a dialog, by measuring the importance and semantic closeness of each generated token. Experiments on the MultiWoZ dataset show our new training algorithm, Keywords Reinforcement Learning with Next-word Sampling (KRLS), achieves state-of-the-art performance on the end-to-end response generation task, with a 15% training time reduction compared to a standard RL algorithm using auto-regressive generation.

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