The language model is a widely used component in fields such as natural language processing, automatic speech recognition, and optical character recognition. In particular, statistical machine translation uses language models, and the translation speed and the amount of memory required are greatly affected by the performance of the language model implementation. We propose a fast and compact implementation of n -gram language models that increases query speed and reduces memory usage by using a double-array structure, which is known to be a fast and compact trie data structure. We propose two types of implementation: one for backward suffix trees and the other for reverse tries. The data structure is optimized for space efficiency by embedding model parameters into otherwise unused spaces in the double-array structure. We show that the reverse trie version of our method is among the smallest state-of-the-art implementations in terms of model size with almost the same speed as the implementation that performs fastest on perplexity calculation tasks. Similarly, we achieve faster decoding while keeping compact model sizes, and we confirm that our method can utilize the efficiency of the double-array structure to achieve a balance between speed and size on translation tasks.

N gram language models tend to increase in size with inflating the corpus size, and consume considerable resources.In this paper, we propose an efficient method for implementing ngram models based on doublearray structures.First, we propose a method for representing backwards suffix trees using double-array structures and demonstrate its efficiency.Next, we propose two optimization methods for improving the efficiency of data representation in the double-array structures.Embedding probabilities into unused spaces in double-array structures reduces the model size.Moreover, tuning the word IDs in the language model makes the model smaller and faster.We also show that our method can be used for building large language models using the division method.Lastly, we show that our method outperforms methods based on recent related works from the viewpoints of model size and query speed when both optimization methods are used.