Variational model for low-resource natural language generation in spoken dialogue systems

2021 ◽  
Vol 65 ◽  
pp. 101120
Author(s):  
Van-Khanh Tran ◽  
Le-Minh Nguyen
Keyword(s):  
Natural Language ◽  
Natural Language Generation ◽  
Variational Model ◽  
Dialogue Systems ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue ◽  
Language Generation ◽  
Low Resource

scholarly journals Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems

2018 ◽  
Author(s):  
Bo-Hsiang Tseng ◽  
Florian Kreyssig ◽  
Paweł Budzianowski ◽  
Iñigo Casanueva ◽  
Yen-Chen Wu ◽  
...  
Keyword(s):  
Natural Language ◽  
Natural Language Generation ◽  
Dialogue Systems ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue ◽  
Language Generation ◽  
Cross Domain

scholarly journals Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems

2015 ◽  
Author(s):  
Tsung-Hsien Wen ◽  
Milica Gasic ◽  
Nikola Mrkšić ◽  
Pei-Hao Su ◽  
David Vandyke ◽  
...  
Keyword(s):  
Natural Language ◽  
Natural Language Generation ◽  
Dialogue Systems ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue ◽  
Language Generation

scholarly journals Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems

2019 ◽  
Author(s):  
Fei Mi ◽  
Minlie Huang ◽  
Jiyong Zhang ◽  
Boi Faltings
Keyword(s):  
Natural Language ◽  
Natural Language Generation ◽  
Dialogue Systems ◽  
Language Generation ◽  
Low Resource ◽  
Recent Success ◽  
Resource Setting ◽  
Meta Learning ◽  
Training Examples ◽  
Task Oriented

Natural language generation (NLG) is an essential component of task-oriented dialogue systems. Despite the recent success of neural approaches for NLG, they are typically developed for particular domains with rich annotated training examples. In this paper, we study NLG in a low-resource setting to generate sentences in new scenarios with handful training examples. We formulate the problem from a meta-learning perspective, and propose a generalized optimization-based approach (Meta-NLG) based on the well-recognized model-agnostic meta-learning (MAML) algorithm. Meta-NLG defines a set of meta tasks, and directly incorporates the objective of adapting to new low-resource NLG tasks into the meta-learning optimization process. Extensive experiments are conducted on a large multi-domain dataset (MultiWoz) with diverse linguistic variations. We show that Meta-NLG significantly outperforms other training procedures in various low-resource configurations. We analyze the results, and demonstrate that Meta-NLG adapts extremely fast and well to low-resource situations.

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