Long short-term memory language models with additive morphological features for automatic speech recognition

Models of morphologically rich languages suffer from data sparsity when words are treated as atomic units. Word-based language models cannot transfer knowledge from common word forms to rarer variant forms. Learning a continuous vector representation of each morpheme allows a compositional model to represent a word as the sum of its constituent morphemes´ vectors. Rare and unknown words containing common morphemes can thus be represented with greater fidelity despite their sparsity. Our novel neural network language model integrates this additive morphological representation into a long short-term memory architecture, improving Russian speech recognition word error rates by 0.9 absolute, 4.4% relative, compared to a robust n-gram baseline model.