Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
Experiments with a Hindi-to-English transfer-based MT system under a miserly data scenario
ACM Transactions on Asian Language Information Processing (TALIP)
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Sample Selection for Statistical Parsing
Computational Linguistics
Active learning for HPSG parse selection
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
A hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Proactive learning: cost-sensitive active learning with multiple imperfect oracles
Proceedings of the 17th ACM conference on Information and knowledge management
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
| Hi-index | 0.00 |
Building machine translation (MT) for many minority languages in the world is a serious challenge. For many minor languages there is little machine readable text, few knowledgeable linguists, and little money available for MT development. For these reasons, it becomes very important for an MT system to make best use of its resources, both labeled and unlabeled, in building a quality system. In this paper we argue that traditional active learning setup may not be the right fit for seeking annotations required for building a Syntax Based MT system for minority languages. We posit that a relatively new variant of active learning, Proactive Learning, is more suitable for this task.