Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems
By Bo-Hsiang Tseng, Florian Kreyssig, Paweł Budzianowski, Iñigo Casanueva, Yen-Chen Wu, Stefan Ultes, Milica Gasic. 2018
14 citations recorded by the source corpus.
Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling. Given a semantic representation provided by the dialogue manager, the language generator should generate sentences that convey desired information. Traditional template-based generators can produce sentences with all necessary information, but these sentences are not sufficiently diverse. With RNN-based models, the diversity of the generated sentences can be high, however, in the process some information is lost. In this work, we improve an RNN-based generator by considering latent information at the sentence level during generation using conditional variational auto-encoder architecture. We demonstrate that our model outperforms the original RNN-based generator, while yielding highly diverse sentences. In addition, our model performs better when the training data is limited.
Subfields: Artificial Intelligence
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Citations in the corpus
- Continual Learning for Natural Language Generation in Task-oriented Dialog Systems (2020, methodology): “A series of neural methods have been proposed to generate accurate, natural, and diverse utterances, including HLSTM (Wen et al., 2015a), SCLSTM (Wen et al., 2015c), Enc-Dec (Wen et al., 2015b), RALSTM (Tran and Nguyen, 2017), SC-VAE (Tseng et al., 2018).”
- Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems (2019, methodology): “Recently, various neural approaches [Wen et al. , 2015c; Tran and Nguyen, 2017; Tseng et al. , 2018] have been proposed to generate accurate, natural and diverse utterances.”