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

We present a neural model for morphological inflection generation which employs a hard
attention mechanism, inspired by the nearly-monotonic alignment commonly found between …

Morphological inflection generation is the task of generating the inflected form of a given
lemma corresponding to a particular linguistic transformation. We model the problem of …

Machine translation can be considered a highly interdisciplinary and multidisciplinary field
because it is approached from the point of view of human translators, engineers, computer …

We improve the quality of statistical machine translation (SMT) by applying models that
predict word forms from their stems using extensive morphological and syntactic information …

We address the problem of translating from morphologically poor to morphologically rich
languages by adding per-word linguistic information to the source language. We use the …

We investigate different representational granularities for sub-lexical representation in
statistical machine translation work from English to Turkish. We find that (i) representing both …

Transformer based models are the modern work horses for neural machine translation
(NMT), reaching state of the art across several benchmarks. Despite their impressive …

Translation into morphologically rich languages is an important but recalcitrant problem in
MT. We present a simple and effective approach that deals with the problem in two phases …

Regression testing is an important part of software maintenance, but it can also be very
expensive. To reduce this expense, software testers may prioritize their test cases so that …