Utilizing target-side semantic role labels to assist hierarchical phrase-based machine translation

Authors:
Qin Gao;Stephan Vogel
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
SSST-5 Proceedings of the Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation
Year:
2011

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Abstract

In this paper we present a novel approach of utilizing Semantic Role Labeling (SRL) information to improve Hierarchical Phrase-based Machine Translation. We propose an algorithm to extract SRL-aware Synchronous Context-Free Grammar (SCFG) rules. Conventional Hiero-style SCFG rules will also be extracted in the same framework. Special conversion rules are applied to ensure that when SRL-aware SCFG rules are used in derivation, the decoder only generates hypotheses with complete semantic structures. We perform machine translation experiments using 9 different Chinese-English test-sets. Our approach achieved an average BLEU score improvement of 0.49 as well as 1.21 point reduction in TER.