![]() ![]() Modern Spoken Dialogue Systems (SDS) are typically developed according to a well-defined ontology, which provides a structured representation of the domain data that the dialogue system can talk about, such as searching for a restaurant or shopping for a laptop. ![]() In subjective testing, human judges confirm that the procedure greatly improves generator performance when only a small amount of data is available in the domain. Corpus-based evaluation results show that the proposed procedure can achieve competitive performance in terms of BLEU score and slot error rate while significantly reducing the data needed to train generators in new, unseen domains. ![]() In this procedure, a model is first trained on counterfeited data synthesised from an out-of-domain dataset, and then fine tuned on a small set of in-domain utterances with a discriminative objective function. In this paper, we propose a procedure to train multi-domain, Recurrent Neural Network-based (RNN) language generators via multiple adaptation steps. Therefore, it is important to leverage existing resources and exploit similarities between domains to facilitate domain adaptation. Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains. ©2016 Association for Computational Linguistics. Multi-domain Neural Network Language Generation for Spoken Dialogue Systems Authors ![]()
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