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The goal of language modeling techniques is to capture the statistical andstructural properties of natural languages from training corpora. This tasktypically involves the learning of short range dependencies, which generallymodel the syntactic properties of a language and/or long range dependencies,which are semantic in nature. We propose in this paper a new multi-spanarchitecture, which separately models the short and long context informationwhile it dynamically merges them to perform the language modeling task. This isdone through a novel recurrent Long-Short Range Context (LSRC) network, whichexplicitly models the local (short) and global (long) context using twoseparate hidden states that evolve in time. This new architecture is anadaptation of the Long-Short Term Memory network (LSTM) to take into accountthe linguistic properties. Extensive experiments conducted on the Penn Treebank(PTB) and the Large Text Compression Benchmark (LTCB) corpus showed asignificant reduction of the perplexity when compared to state-of-the-artlanguage modeling techniques.

Long-Short Range Context Neural Networks for Language Modeling