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. 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 subjective testing, human judges confirm that the procedure greatly
improves generator performance when only a small amount of data is available in
the domain.

本研究提供了一种训练多领域、基于递归神经网络的语言生成器的程序，包含了多个适应步骤，并利用伪造数据和领域内句子的优化目标函数，旨在在新的、看不见的领域内显著减少培训的数据需求，其语料库评估结果和主观测试都表示该程序可以在保证 BLEU 分数和槽误差率竞争力的同时大幅提高语言生成器性能。