Paper: Faster Phrase-Based Decoding by Refining Feature State

ACL ID P14-2022
Title Faster Phrase-Based Decoding by Refining Feature State
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

We contribute a faster decoding algo- rithm for phrase-based machine transla- tion. Translation hypotheses keep track of state, such as context for the language model and coverage of words in the source sentence. Most features depend upon only part of the state, but traditional algorithms, including cube pruning, handle state atom- ically. For example, cube pruning will re- peatedly query the language model with hypotheses that differ only in source cov- erage, despite the fact that source cover- age is irrelevant to the language model. Our key contribution avoids this behav- ior by placing hypotheses into equivalence classes, masking the parts of state that matter least to the score. Moreover, we ex- ploit shared words in hypotheses to itera- tively refine language model scores rather tha...