End-to-End Learning of Task-Oriented Dialogs
34 citations recorded by the source corpus.
In this thesis proposal, we address the limitations of conventional pipeline design of task-oriented dialog systems and propose end-to-end learning solutions. We design neural network based dialog system that is able to robustly track dialog state, interface with knowledge bases, and incorporate structured query results into system responses to successfully complete task-oriented dialog. In learning such neural network based dialog systems, we propose hybrid offline training and online interactive learning methods. We introduce a multi-task learning method in pre-training the dialog agent in a supervised manner using task-oriented dialog corpora. The supervised training agent can further be improved via interacting with users and learning online from user demonstration and feedback with imitation and reinforcement learning. In addressing the sample efficiency issue with online policy learning, we further propose a method by combining the learning-from-user and learning-from-simulation approaches to improve the online interactive learning efficiency.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Natural Language Processing
Citations in the corpus
- A Simple Language Model for Task-Oriented Dialogue (2020, background): “The modular dependencies of these components can lead to error propagation when information is not provided to subsequent modules in the pipeline [27].”
- Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning (2019, methodology): “…et al., 2009; Reiter and Dale, 2000; Rieser and Lemon, 2010), especially when adopting deep learning models (Deng and Liu, 2018; Mesnil et al., 2015; Wen et al., 2015, 2017; Su et al., 2017; Papangelis et al., 2018; Liu and Lane, 2018b; Li et al., 2017; Williams et al., 2017; Liu and Lane, 2018a).”
- Incorporating the Structure of the Belief State in End-to-End Task-Oriented Dialogue Systems (2018, background): “Our work is also akin to modularly connected end-to-end trainable networks [23, 22, 13, 12, 11].”
- Modeling Multi-Action Policy for Task-Oriented Dialogues (2019, methodology): “Recent work (Wen et al., 2017; Liu and Lane, 2018) also used SL as pre-training for RL to mitigate the sample inefficiency of RL approaches and to reduce the number of interactions.”