Paper: Better Word Alignments with Supervised ITG Models

ACL ID P09-1104
Title Better Word Alignments with Supervised ITG Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009

This work investigates supervised word align- ment methods that exploit inversion transduc- tion grammar (ITG) constraints. We con- sider maximum margin and conditional like- lihood objectives, including the presentation of a new normal form grammar for canoni- calizing derivations. Even for non-ITG sen- tence pairs, we show that it is possible learn ITG alignment models by simple relaxations of structured discriminative learning objec- tives. For efficiency, we describe a set of prun- ing techniques that together allow us to align sentences two orders of magnitude faster than naive bitext CKY parsing. Finally, we intro- duce many-to-one block alignment features, which significantly improve our ITG models. Altogether, our method results in the best re- ported AER numbers for Chinese-Englis...