Date of Original Version
Prague Journal of Mathematical Linguistics
© 2013 PBML
Abstract or Description
We present morphogen, a tool for improving translation into morphologically rich languages with synthetic phrases. We approach the problem of translating into morphologically rich languages in two phases. First, an inflection model is learned to predict target word inflections from source side context. Then this model is used to create additional sentence specific translation phrases. These “synthetic phrases” augment the standard translation grammars and decoding proceeds normally with a standard translation model. We present an open source Python implementation of our method, as well as a method of obtaining an unsupervised morphological analysis of the target language when no supervised analyzer is available.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Prague Journal of Mathematical Linguistics, 100, 1, 51-62.