Translating into Morphologically Rich Languages with Synthetic Phrases

Class-Based Language Modeling for Translating into Morphologically Rich Languages

An Evaluation of Subword Segmentation Strategies for Neural Machine Translation of Morphologically Rich Languages

Byte-Pair Encoding (BPE) (Sennrich et al., 2016) has become a standard pre-processing step when building neural machine translation systems. However, it is not clear whether this is an optimal strategy in all settings. W…

Role of Morphology Injection in Statistical Machine Translation

Phrase-based Statistical models are more commonly used as they perform optimally in terms of both, translation quality and complexity of the system. Hindi and in general all Indian languages are morphologically richer th…

Tailoring Neural Architectures for Translating from Morphologically Rich Languages

A morphologically complex word (MCW) is a hierarchical constituent with meaning-preserving subunits, so word-based models which rely on surface forms might not be powerful enough to translate such structures. When transl…

Word Representation Models for Morphologically Rich Languages in Neural Machine Translation

Dealing with the complex word forms in morphologically rich languages is an open problem in language processing, and is particularly important in translation. In contrast to most modern neural systems of translation, whi…