Dynamic topic adaptation for improved contextual modelling in statistical machine translation
In recent years there has been an increased interest in domain adaptation techniques for statistical machine translation (SMT) to deal with the growing amount of data from different sources. Topic modelling techniques applied to SMT are closely related to the field of domain adaptation but more flexible in dealing with unstructured text. Topic models can capture latent structure in texts and are therefore particularly suitable for modelling structure in between and beyond corpus boundaries, which are often arbitrary. In this thesis, the main focus is on dynamic translation model adaptation to texts of unknown origin, which is a typical scenario for an online MT engine translating web documents. We introduce a new bilingual topic model for SMT that takes the entire document context into account and for the first time directly estimates topic-dependent phrase translation probabilities in a Bayesian fashion. We demonstrate our modelâs ability to improve over several domain adaptation baselines and further provide evidence for the advantages of bilingual topic modelling for SMT over the more common monolingual topic modelling. We also show improved performance when deriving further adapted translation features
Hasler, Eva Cornelia
machine translation ; word sense disambiguation ; topic modelling
Dynamic topic adaptation for improved contextual modelling in statistical machine translation. In recent years there has been an increased interest in domain adaptation techniques for statistical machine translation (SMT) to deal with the growing amount of data from different sources. Topic modelling techniques applied to SMT are closely related to the field of domain adaptation but more flexible in dealing with unstructured text. Topic models can capture latent structure in texts and are therefore particularly suitable for modelling structure in between and beyond corpus boundaries, which are often arbitrary. In this thesis, the main focus is on dynamic translation model adaptation to texts of unknown origin, which is a typical scenario for an online MT engine translating web documents. We introduce a new bilingual topic model for SMT that takes the entire document context into account and for the first time directly estimates topic-dependent phrase translation probabilities in a Bayesian fashion. We demonstrate our modelâs ability to improve over several domain adaptation baselines and further provide evidence for the advantages of bilingual topic modelling for SMT over the more common monolingual topic modelling. We also show improved performance when deriving further adapted translation features
dynamic topic adaptation improve contextual modelling statistical_machine_translation increase_interest domain adaptation technique statistical_machine translation_smt deal grow amount_data different_source topic model technique_apply smt closely_related field domain adaptation flexible dealing unstructured text topic model capture latent structure text therefore particularly suitable modelling structure beyond corpus boundary often arbitrary main_focus dynamic translation model adaptation texts unknown origin typical scenario online engine translate web document introduce_new bilingual topic model smt take entire document context account first_time directly estimate topic dependent phrase translation probability bayesian fashion model ability improve several domain adaptation baseline provide_evidence advantage bilingual topic model smt common monolingual topic modelling also_show improved performance adapt translation feature