Unbiased sampling with importance weighting can replace threshold pruning in transduced language models, giving you both accurate probability estimates and quantifiable error bounds—critical for applications like DNA-to-protein translation.
This paper solves a key problem in transduced language models: computing probabilities for target strings when a finite-state transducer maps many (possibly infinite) source strings to the same target.