Faster Phrase-Based Decoding by Refining Feature State

Kenneth Heafield, Michael Kayser, Christopher D. Manning

Abstract: We contribute a faster decoding algorithm for phrase-based machine translation. 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 atomically. For example, cube pruning will repeatedly query the language model with hypotheses that differ only in source coverage, despite the fact that source coverage is irrelevant to the language model. Our key contribution avoids this behavior by placing hypotheses into equivalence classes, masking the parts of state that matter least to the score. Moreover, we exploit shared words in hypotheses to iteratively refine language model scores rather than handling language model state atomically. Since our algorithm and cube pruning are both approximate, improvement can be used to increase speed or accuracy. When tuned to attain the same accuracy, our algorithm is 4.0‐7.7 times as fast as the Moses decoder with cube pruning.