This book compares and contrasts the principles and practices of rule-based machine
translation (RBMT), statistical machine translation (SMT), and example-based machine …

The correct translation of verb tenses ensures that the temporal ordering of events in the
source text is maintained in the target text. This paper assesses the utility of automatically …

Résumé Machine Translation (MT) has progressed tremendously in the past two decades.
The rule-based and interlingua approaches have been superseded by statistical models …

The recent technological shift in machine translation from statistical machine translation
(SMT) to neural machine translation (NMT) raises the question of the strengths and …

When translating into morphologically rich languages, statistical MT approaches face the
problem of data sparsity. The severity of the sparseness problem will be high when the …

Formally, Machine Translation (MT) is a subfield of computational linguistics that
investigates the use of software to translate text or speech from one natural language to …

This paper describes our experience using available linguistic resources for Croatian in
order to address data sparseness when building an English-to-Croatian general domain …

We present eman, a tool for managing large numbers of computational experiments. Over
the years of our research in machine translation (MT), we have collected a couple of ideas …

SMT approaches face the problem of data sparsity while translating into a morphologically
rich language. It is very unlikely for a parallel corpus to contain all morphological forms of …

deterministically converted into word forms by a morphological analyser. AU the discussed
enhancements seem to help only under little-to-medium training data conditions, but stop to …