Principled Induction of Phrasal Bilexica
Markus Saers, Dekai Wu · 2011
We aim to replace the long and complicated, pipeline employed to produce probabilistic phrasal bilexica with a theoretically principled, grammar based, approach. To this end, we introduce a learning regime to learn a phrasal grammar equivalent to linear transduction grammars. The stochastic version of this new grammar type also has the property that the set of biterminals constitute a natural probability distribution, making it similar to a probabilistic translation lexicon. Since we learn a phrasal grammar, we are, in effect, learning a probabilistic phrasal bilexicon. As a proof of concept, we show that phrasal bilexica, induced in this manner, can be used to improve the performance of a traditional phrase-based SMT system. 1