Year: 2007
Type: conference-paper
Abstract: In this paper, we investigate the use of linguistically motivated and computationally efficient structured language models for reranking N-best hypotheses in a statistical machine translation system. These language models, developed from Constraint Dependency Grammar parses, tightly integrate knowledge of words, morphological and lexical features, and syntactic dependency constraints. Two structured language models are applied for N-best rescoring, one is an almost-parsing language model, and the other utilizes more syntactic features by explicitly modeling syntactic dependencies between words. We also investigate effective and efficient language modeling methods to use N-grams extracted from up to 1 teraword of web documents. We apply all these language models for N-best re-ranking on the NIST and DARPA GALE program12006 and 2007 machine translation evaluation tasks and find that the combination of these language models increases the BLEU score up to 1.6% absolutely on blind test sets.
Authors: Wen Wang, Andreas Stolcke, Jing Zheng
Institution: SRI International
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2140119717