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This paper presents a scalable method for integrating compositionalmorphological representations into a vector-based probabilistic language model.Our approach is evaluated in the context of log-bilinear language models,rendered suitably efficient for implementation inside a machine translationdecoder by factoring the vocabulary. We perform both intrinsic and extrinsicevaluations, presenting results on a range of languages which demonstrate thatour model learns morphological representations that both perform well on wordsimilarity tasks and lead to substantial reductions in perplexity. When usedfor translation into morphologically rich languages with large vocabularies,our models obtain improvements of up to 1.2 BLEU points relative to a baselinesystem using back-off n-gram models.

Compositional Morphology for Word Representations and Language Modelling