Dual Latent Variable Model for Low-Resource Natural Language Generation in Dialogue Systems

By Van-Khanh Tran, Le-Minh Nguyen. 2018

13 citations recorded by the source corpus.

Recent deep learning models have shown improving results to natural language generation (NLG) irrespective of providing sufficient annotated data. However, a modest training data may harm such models’ performance. Thus, how to build a generator that can utilize as much of knowledge from a low-resource setting data is a crucial issue in NLG. This paper presents a variational neural-based generation model to tackle the NLG problem of having limited labeled dataset, in which we integrate a variational inference into an encoder-decoder generator and introduce a novel auxiliary auto-encoding with an effective training procedure. Experiments showed that the proposed methods not only outperform the previous models when having sufficient training dataset but also demonstrate strong ability to work acceptably well when the training data is scarce.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Fei Mi
2. Minlie Huang
3. Jiyong Zhang
4. Liangwei Chen
5. Boi Faltings
6. Mengjie Zhao
7. Xiaoyan Zhu
8. Ondrej Dusek

Citations in the corpus

• Continual Learning for Natural Language Generation in Task-oriented Dialog Systems (2020, background): “They later designed two domain adaptation critics with an adversarial training algorithm (Tran and Nguyen, 2018a).”
• Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems (2019, methodology): “They later designed two domain adaptation critics with an adversarial training algo-rithm [Tran and Nguyen, 2018a] to learn an indistinguishable latent representation of the source and the target domain to better generalize to the target domain.”