Multi-domain Neural Network Language Generation for Spoken Dialogue Systems

Understand

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.

• Therefore, it is important to leverage existing resources and exploit similarities between domains to facilitate domain adaptation.
• In this paper, we propose a procedure to train multi-domain, Recurrent Neural Network-based (RNN) language generators via multiple adaptation steps.
• 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.