Domain Adaptation Machine Translation
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Domain adaptation machine translation. Often there is a mismatch between the domain for which training data are available and the target domain of a machine translation system. Full text available as. The recent success of neural machine translation models relies on the availability of high quality in domain data.
Domain adaptation da techniques in machine translation mt have been widely studied. Neural machine translation nmt is a deep learning based approach for machine translation which yields the state of the art translation performance in scenarios where large scale parallel corpora are available. Three main topics can be identified depending on the availability of domain specific data.
Domain adaptation of neural machine translation by lexicon induction junjie hu mengzhou xia graham neubig jaime carbonell language technologies institute school of computer science carnegie mellon university fjunjieh gneubig jgcg cs cmu edu mengzhox andrew cmu edu abstract it has been previously noted that neural ma. Domain adaptation is a very active research topic within the area of smt. Domain adaptation in statistical machine translation.
Phd thesis dublin city university. Previous unsupervised domain adaptation strategies include training the model with in domain copied monolingual or back translated data. However these methods use generic representations for text.
Domain adaptation is required when domain specific data is scarce or nonexistent. For statistical machine translation smt several da methods have been proposed to overcome the lack of domain speciļ¬c data. 0000 0001 5659 5865 2017 domain adaptation for statistical machine translation and neural machine translation.
For exam ple self training 1 2 uses a mt system trained on general corpus to translate in domain monolingual data as additional. Domain adaptation is the main subject of 143 publications. Different domains may vary by topic or text style.
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