Against this background, we consider a radically different approach to language modelling: instead of explicitly storing all distinct n-grams, we store a randomised representation.
we show that the Bloom filter (Bloom (1970); BF), a simple space-efficient randomised data structure for representing sets, may be used to represent statistics from larger corpora and for higher-order n-grams to complement a conventional smoothed trigram model within an SMT decoder.