Unsupervised Syntactic Alignment with Inversion Transduction Grammars
Adam Pauls, Dan Klein, David Chiang, Kevin K. Knight · 2010
Syntactic machine translation systems cur-rently use word alignments to infer syntactic correspondences between the source and tar-get languages. Instead, we propose an un-supervised 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 possi-ble, 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. 1