| Constraints on Inter-Domain Variability | As we discussed in the introduction, our goal is to provide a method for domain adaptation based on semi-supervised learning of models with distributed representations . |
| Discussion and Conclusions | In this paper we presented a domain-adaptation method based on semi-supervised learning with distributed representations coupled with constraints favoring domain-independence of modeled phenomena. |
| Introduction | Such LVMs can be regarded as composed of two parts: a mapping from initial (normally, word-based) representation to a new shared distributed representation , and also a classifier in this representation. |
| Related Work | Semi-supervised leam-ing with distributed representations and its application to domain adaptation has previously been considered in (Huang and Yates, 2009), but no attempt has been made to address problems specific to the domain-adaptation setting. |
| The Latent Variable Model | The adaptation method advocated in this paper is applicable to any joint probabilistic model which uses distributed representations , i.e. |