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Modeling Target-Side Morphology in Neural Machine Translation: A Comparison of Strategies
Key challenges of rich target-side morphology in data-driven machine translation include: (1) A large amount of differently inflected word surface forms entails a larger vocabulary and thus data sparsity.
Modeling Word Formation in English--German Neural Machine Translation
This paper studies strategies to model word formation in NMT using rich linguistic information, namely a word segmentation approach that goes beyond splitting into substrings by considering fusional morphology.
Modeling Target-Side Inflection in Neural Machine Translation
NMT systems have problems with large vocabulary sizes.
Addressing Problems across Linguistic Levels in SMT: Combining Approaches to Model Morphology, Syntax and Lexical Choice
Many errors in phrase-based SMT can be attributed to problems on three linguistic levels: morphological complexity in the target language, structural differences and lexical choice.
Simple Compound Splitting for German
This paper presents a simple method for German compound splitting that combines a basic frequency-based approach with a form-to-lemma mapping to approximate morphological operations.
