Dialogue Strategy Adaptation to New Action Sets Using Multi-dimensional Modelling

• URL: http://arxiv.org/abs/2204.07082v1
• Date: Thu, 14 Apr 2022 16:26:22 GMT
• Title: Dialogue Strategy Adaptation to New Action Sets Using Multi-dimensional Modelling
• Authors: Simon Keizer, Norbert Braunschweiler, Svetlana Stoyanchev, Rama Doddipatla
• Abstract summary: A major bottleneck for building statistical spoken dialogue systems is the need for large amounts of training data. We adopt the multi-dimensional approach to dialogue management and evaluate its potential for transfer learning. Specifically, we exploit pre-trained task-independent policies to speed up training for an extended task-specific action set.
• Score: 15.575400480417844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A major bottleneck for building statistical spoken dialogue systems for new domains and applications is the need for large amounts of training data. To address this problem, we adopt the multi-dimensional approach to dialogue management and evaluate its potential for transfer learning. Specifically, we exploit pre-trained task-independent policies to speed up training for an extended task-specific action set, in which the single summary action for requesting a slot is replaced by multiple slot-specific request actions. Policy optimisation and evaluation experiments using an agenda-based user simulator show that with limited training data, much better performance levels can be achieved when using the proposed multi-dimensional adaptation method. We confirm this improvement in a crowd-sourced human user evaluation of our spoken dialogue system, comparing partially trained policies. The multi-dimensional system (with adaptation on limited training data in the target scenario) outperforms the one-dimensional baseline (without adaptation on the same amount of training data) by 7% perceived success rate.

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