Domain Adaptation arises when we aim at learning from source domain a model that can perform acceptably well on a different target domain. It is especially crucial for Natural Language Generation (NLG) in Spoken Dialogue Systems when there are sufficient annotated data in the source domain, but there is a limited labeled data in the target domain. How to effectively utilize as much of existing abilities from source domains is a crucial issue in domain adaptation. In this paper, we propose an adversarial training procedure to train a Variational encoder-decoder based language generator via multiple adaptation steps. In this procedure, a model is first trained on a source domain data and then fine-tuned on a small set of target domain utterances under the guidance of two proposed critics. Experimental results show that the proposed method can effectively leverage the existing knowledge in the source domain to adapt to another related domain by using only a small amount of in-domain data.This work is licensed under a Creative Commons Attribution 4.0 International License. License details: in a new, unseen domain by using a limited amount of in-domain data; (4) We investigate the effectiveness of the proposed method in different scenarios, including ablation, domain adaptation, scratch, and unsupervised training with various amount of data.
Related WorkGenerally, Domain Adaptation involves two different types of datasets, one from a source domain and the other from a target domain. The source domain typically contains a sufficient amount of annotated data such that a model can be efficiently built, while there is often little or no labeled data in the target domain. Domain adaptation for NLG have been less studied despite its important role in developing multi-domain SDS. Walker et al. (2001) proposed a SPoT-based generator to address domain adaptation problems. Subsequently, a system focused on tailoring user preferences (Walker et al., 2007), and controlling user perceptions of linguistic style (Mairesse and Walker, 2011). Moreover, a phrase-based statistical generator (Mairesse et al., 2010) using graphical models and active learning, and a multi-domain procedure (Wen et al., 2016a) via data counterfeiting and discriminative training. Neural variational framework for generative models of text have been studied longitudinally. Chung et al. (2015) proposed a recurrent latent variable model VRNN for sequential data by integrating latent random variables into hidden state of a RNN model. A hierarchical multi scale recurrent neural networks was proposed to learn both hierarchical and temporal representation (Chung et al., 2016). Zhang et al. (2016) introduced a variational neural machine translation that incorporated a continuous latent variable to model underlying semantics of sentence pairs. Bowman et al. (2015) presented a variational autoencoder for unsupervised generative language model.Adversarial adaptation methods have shown promising improvement in many machine learning applications despite the presence of dom...
Our results suggest that the combination of STR and MLPA could be a rapid, reliable, and affordable detection protocol for determination of the carrier's status and prenatal diagnosis of DMD in a developing country such as Vietnam.
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