Unsupervised Syntactic Alignment with Inversion Transduction Grammars

Syntactic machine translation systems currently use word alignments to infer syntactic correspondences between the source and target languages. Instead, we propose an unsupervised ITG alignment model that directly aligns syntactic structures. Our model aligns spans in a source sentence to nodes in a target parse tree. We show that our model produces syntactically consistent analyses where possible, while being robust in the face of syntactic divergence. Alignment quality and end-to-end translation experiments demonstrate that this consistency yields higher quality alignments than our baseline.

• Type: Journal
• Year: 2010
• Where: NAACL
• Authors: Adam Pauls, Dan Klein, David Chiang, Kevin Knight
• Keywords: Computational Linguistics, ITG Alignment Model, NAACL 2010, Syntactic Correspondences, Syntactic Machine Translation