Incremental syntactic language models for phrase-based translation

  • Authors:
  • Lane Schwartz;Chris Callison-Burch;William Schuler;Stephen Wu

  • Affiliations:
  • Air Force Research Laboratory, Wright-Patterson AFB, OH;Johns Hopkins University, Baltimore, MD;Ohio State University, Columbus, OH;Mayo Clinic, Rochester, MN

  • Venue:
  • HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper describes a novel technique for incorporating syntactic knowledge into phrase-based machine translation through incremental syntactic parsing. Bottom-up and top-down parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, which generates partial hypothesized translations from left-to-right. Incremental syntactic language models score sentences in a similar left-to-right fashion, and are therefore a good mechanism for incorporating syntax into phrase-based translation. We give a formal definition of one such lineartime syntactic language model, detail its relation to phrase-based decoding, and integrate the model with the Moses phrase-based translation system. We present empirical results on a constrained Urdu-English translation task that demonstrate a significant BLEU score improvement and a large decrease in perplexity.