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arXiv Computation and Language By V\'esteinn Sn{\ae}bjarnarson, Samuel Kiegeland, Manuel de Prada Corral, Ryan Cotterell, Tim Vieira

Stochastic Estimation of Transduced Language Models

Read the original on arXiv Computation and Language →

Transduced language models (TLMs) combine a pretrained source language model with a finite‑state transducer to produce a language model over target strings. The paper introduces an unbiased stochastic estimator that resamples source prefixes without replacement and reweights them, allowing accurate estimation of target prefix probabilities while reducing computation compared to threshold‑pruned beam summing. Experiments on encyclopedic text, DNA, and DNA‑to‑amino‑acid transduction show improved compute–variance trade‑offs and significant runtime reductions, and the method also lowers estimated corpus surprisal in a reading‑time analysis without altering its conclusions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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