Modeling Multi-Action Policy for Task-Oriented Dialogues

By Lei Shu, Hu Xu, Bing Liu, Piero Molino. 2019

11 citations recorded by the source corpus.

Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system. In most existing approaches, the agent predicts only one DM policy action per turn. This significantly limits the expressive power of the conversational agent and introduces unwanted turns of interactions that may challenge users’ patience. Longer conversations also lead to more errors and the system needs to be more robust to handle them. In this paper, we compare the performance of several models on the task of predicting multiple acts for each turn. A novel policy model is proposed based on a recurrent cell called gated Continue-Act-Slots (gCAS) that overcomes the limitations of the existing models. Experimental results show that gCAS outperforms other approaches. The datasets and code are available at https://leishu02.github.io/.

Subfields: Artificial Intelligence

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Citations in the corpus

Dynamic Dialogue Policy for Continual Reinforcement Learning (2022, methodology): “Similar to existing work (Shu et al., 2019; Zhang et al., 2020a) we separately predict domains, intents and slots for action prediction.”
Dynamic Dialogue Policy Transformer for Continual Reinforcement Learning (2022, background): “Similar to existing work (Shu et al., 2019; Zhang et al., 2020a) we separately predict domains, in-tents and slots for action prediction.”
Lifelong Knowledge Learning in Rule-based Dialogue Systems (2020, background): “Recently, deep learning models are the mainstream in research (Vinyals and Le, 2015; Xing et al., 2017; Li et al., 2017, Shu et al, 2019).”