A Scalable Distributed Syntactic, Semantic, and Lexical Language Model

作者：Tan Ming^*; Zhou Wenli; Zheng Lei; Wang Shaojun

来源：Computational Linguistics, 2012, 38(3): 631-671.

DOI：10.1162/COLI_a_00107

This paper presents an attempt at building a large scale distributed composite language model that is formed by seamlessly integrating an n-gram model, a structured language model, and probabilistic latent semantic analysis under a directed Markov random field paradigm to simultaneously account for local word lexical information, mid-range sentence syntactic structure, and long-span document semantic content. The composite language model has been trained by performing a convergent N-best list approximate EM algorithm and a follow-up EM algorithm to improve word prediction power on corpora with up to a billion tokens and stored on a supercomputer. The large scale distributed composite language model gives drastic perplexity reduction over n-grams and achieves significantly better translation quality measured by the Bleu score and %26quot;readability%26quot; of translations when applied to the task of re-ranking the N-best list from a state-of-the-art parsing-based machine translation system.

• 出版日期2012-9