Corpus based approaches to automatic translation such as Example Based and Statistical
Machine Translation systems use large amounts of parallel data created by humans to train …

Parallel corpus is an indispensable resource for translation model training in statistical
machine translation (SMT). Instead of collecting more and more parallel training corpora …

We present an empirical study of instance selection techniques for machine translation. In
an active learning setting, instance selection minimizes the human effort by identifying the …

Statistical machine translation relies heavily on the available training data. In some cases it
is necessary to limit the amount of training data that can be created for or actually used by …

We introduce FDA5 for efficient parameterization, optimization, and implementation of
feature decay algorithms (FDA), a class of instance selection algorithms that use feature …

Neural machine translation (NMT) normally requires a large bilingual corpus to train a high-
translation-quality model. However, building such parallel corpora for many low-resource …

To adapt a translation model trained from the data in one domain to another, previous works
paid more attention to the studies of parallel corpus while ignoring the in-domain …

This paper investigates active learning to improve statistical machine translation (SMT) for
low-resource language pairs, ie, when there is very little pre-existing parallel text. Since …

In machine-learning applications, data selection is of crucial importance if good runtime
performance is to be achieved. In a scenario where the test set is accessible when the …

Performance of statistical machine translation (SMT) systems relies on the availability of a
large parallel corpus which is used to estimate translation probabilities. However, the …