Statistical machine translation (SMT) treats the translation of natural language as a machine
learning problem. By examining many samples of human-produced translation, SMT …

The development of deep learning techniques has allowed Neural Machine Translation
(NMT) models to become extremely powerful, given sufficient training data and training time …

We explore efficient domain adaptation for the task of statistical machine translation based
on extracting sentences from a large generaldomain parallel corpus that are most relevant to …

(57) ABSTRACT A method for terminological adaptation includes receiving a Vocabulary
pair including source and target language terms. Each term is in a class which includes a set …

Abstract Domain adaptation has recently gained interest in statistical machine translation to
cope with the performance drop observed when testing conditions deviate from training …

In this paper we present experiments concerning translation model adaptation for statistical
machine translation. We develop a method to adapt translation models using information …

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

This paper compares techniques to combine diverse parallel corpora for domain-specific
phrase-based SMT system training. We address a common scenario where little in-domain …

We explore unsupervised language model adaptation techniques for Statistical Machine
Translation. The hypotheses from the machine translation output are converted into queries …

Objective We investigate machine translation (MT) of user search queries in the context of
cross-lingual information retrieval (IR) in the medical domain. The main focus is on …