Automatic Rule Learning for Resource-Limited MT
AMTA '02 Proceedings of the 5th Conference of the Association for Machine Translation in the Americas on Machine Translation: From Research to Real Users
A hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Machine translation using probabilistic synchronous dependency insertion grammars
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
An end-to-end discriminative approach to machine translation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Relabeling syntax trees to improve syntax-based machine translation quality
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
The MILE corpus for less commonly taught languages
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Combining morphosyntactic enriched representation with n-best reranking in statistical translation
SSST '07 Proceedings of the NAACL-HLT 2007/AMTA Workshop on Syntax and Structure in Statistical Translation
ParaMor: minimally supervised induction of paradigm structure and morphological analysis
SigMorPhon '07 Proceedings of Ninth Meeting of the ACL Special Interest Group in Computational Morphology and Phonology
| Hi-index | 0.00 |
Syntax-based Machine Translation systems have recently become a focus of research with much hope that they will outperform traditional Phrase-Based Statistical Machine Translation (PBSMT). Toward this goal, we present a method for analyzing the morphosyntactic content of language from an Elicitation Corpus such as the one included in the LDC's upcoming LCTL language packs. The presented method discovers a mapping between morphemes and linguistically relevant features. By providing this tool that can augment structure-based MT models with these rich features, we believe the discriminative power of current models can be improved. We conclude by outlining how the resulting output can then be used in inducing a morphosyntactically feature-rich grammar for AVENUE, a modern syntax-based MT system.