Domain and genre dependency in Statistical Machine Translation
Statistical Machine Translation (SMT) is currently the most promising and widely studied paradigm in the broader field of Machine Translation, continuously explored in order to improve its performance and to find solutions to its current shortcomings, in particular the sparsity of big bilingual corpora in a variety of domains or genres to be used as training data. However, while one the main trends is still to rely as much as possible on already available large collections of data, even when they do not fit quite well specific translation tasks in terms of relatedness of content, the possibility of using less but appropriately selected training sets - depending on the textual variety of the documents that need to be translated case by case - has not been extensively explored as much so far. The goal of this research is to investigate whether this latter possibility, i.e. the lack of availability of large quantities of assorted data, can have a possible solution in the application of strategies commonly used in genre and domain classification (including unsupervised topic modeling and document dissimilarity techniques), in particular performing subsampling experiments on bilingual corpora in order to obtain
Brunello, Marco
Domain and genre dependency in Statistical Machine Translation. Statistical Machine Translation (SMT) is currently the most promising and widely studied paradigm in the broader field of Machine Translation, continuously explored in order to improve its performance and to find solutions to its current shortcomings, in particular the sparsity of big bilingual corpora in a variety of domains or genres to be used as training data. However, while one the main trends is still to rely as much as possible on already available large collections of data, even when they do not fit quite well specific translation tasks in terms of relatedness of content, the possibility of using less but appropriately selected training sets - depending on the textual variety of the documents that need to be translated case by case - has not been extensively explored as much so far. The goal of this research is to investigate whether this latter possibility, i.e. the lack of availability of large quantities of assorted data, can have a possible solution in the application of strategies commonly used in genre and domain classification (including unsupervised topic modeling and document dissimilarity techniques), in particular performing subsampling experiments on bilingual corpora in order to obtain
domain genre dependency statistical_machine_translation statistical_machine_translation smt currently promise widely study paradigm broad field machine_translation continuously explore order_improve performance solution current shortcoming particular big bilingual corpus variety domains genre use train data however one_main trend still rely much_possible already available large_collection data even fit quite well specific translation_task term relatedness content possibility use less appropriately select training set depend textual variety document need translated case case extensively explore much far goal_investigate whether latter possibility lack availability large_quantity data possible_solution application strategy commonly_use genre domain classification include unsupervised topic model document dissimilarity technique particular perform bilingual corpus order_obtain