Pith. sign in

REVIEW 1 cited by

Semi-Autoregressive Training Improves Mask-Predict Decoding

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2001.08785 v1 pith:5WZG4GUM submitted 2020-01-23 cs.CL cs.LGstat.ML

Marjan Ghazvininejad , Omer Levy , Luke Zettlemoyer This is my paper · ORCID
classification cs.CLcs.LGstat.ML
keywords mask-predictmodelsdecodingsemi-autoregressivetrainingperformancesmartalgorithm
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The recently proposed mask-predict decoding algorithm has narrowed the performance gap between semi-autoregressive machine translation models and the traditional left-to-right approach. We introduce a new training method for conditional masked language models, SMART, which mimics the semi-autoregressive behavior of mask-predict, producing training examples that contain model predictions as part of their inputs. Models trained with SMART produce higher-quality translations when using mask-predict decoding, effectively closing the remaining performance gap with fully autoregressive models.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs

    cs.CL 2025-06 reject novelty 5.0 of 10

    ASTS extends locally typical sampling with semantic scoring and dynamic thresholds, reporting improved perplexity, MAUVE, and diversity on story and summarization tasks.

Pith tools