A Scalable Distributed Syntactic, Semantic, and Lexical Language Model
Abstract
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 readability 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2012
- Accession Number
- ADA563918
Entities
People
- Lei Zheng
- Ming Tan
- Shaojun Wang
- Wenli Zhou
Organizations
- Wright State University