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Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
[article]
2015
arXiv
pre-print
The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical
arXiv:1508.01755v1
fatcat:7trc544s7bepfkbsxqmwbxy22m