Paper: Integrating Joint n-gram Features into a Discriminative Training Framework

ACL ID N10-1103
Title Integrating Joint n-gram Features into a Discriminative Training Framework
Venue Human Language Technologies
Session Main Conference
Year 2010
Authors

Phonetic string transduction problems, such as letter-to-phoneme conversion and name transliteration, have recently received much attention in the NLP community. In the past few years, two methods have come to dom- inate as solutions to supervised string trans- duction: generative joint n-gram models, and discriminative sequence models. Both ap- proaches benefit from their ability to consider large, flexible spans of source context when making transduction decisions. However, they encode this context in different ways, provid- ing their respective models with different in- formation. To combine the strengths of these two systems, we include joint n-gram fea- tures inside a state-of-the-art discriminative sequence model. We evaluate our approach on several letter-to-phoneme and translitera-...