atlas

Research map / Label-efficient and robust learning / Recognition and long-tail

Unsupervised Neural Machine Translation with SMT as Posterior Regularization

AAAI 2019 · Label-efficient and robust learning › Recognition and long-tail · approach: Multilingual language representation pretraining · 55 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
use SMT as posterior regularization in joint EM training with NMTunsupervised NMT · fr-enBLEU: 27.68 -> 28.79Helped
use SMT as posterior regularization in joint EM training with NMTunsupervised NMT · en-frBLEU: 28.11 -> 29.21Helped
use SMT as posterior regularization in joint EM training with NMTunsupervised NMT · de-en newstest 2014BLEU: 10.21 -> 20.04Helped
use SMT as posterior regularization in joint EM training with NMTunsupervised NMT · en-de newstest 2014BLEU: 6.89 -> 16.43Helped
use SMT as posterior regularization in joint EM training with NMTunsupervised NMT · de-en newstest 2016BLEU: 25.19 -> 25.92Helped
use SMT as posterior regularization in joint EM training with NMTunsupervised NMT · en-de newstest 2016BLEU: 20.23 -> 21.07Helped
add R2L regularization after the final training iteration (ablation)Our Method · English-French and English-German test setsBLEU: 28.79 -> 28.92; 29.21 -> 29.53; 20.04 -> 20.43; 16.43 -> 16.97; 25.92 -> 26.32; 21.07 -> 21.65Helped
replace SMT0 warm-up with word-by-word pseudo parallel initialization (ablation)Our Method initialization · fr-en newstest 2014BLEU: 24.06 -> 12.29Hurt
replace SMT0 warm-up with word-by-word pseudo parallel initialization (ablation)Our Method initialization · en-fr newstest 2014BLEU: 24.82 -> 12.46Hurt
replace SMT0 warm-up with word-by-word pseudo parallel initialization (ablation)Our Method initialization · de-en newstest 2014BLEU: 16.29 -> 7.32Hurt
replace SMT0 warm-up with word-by-word pseudo parallel initialization (ablation)Our Method initialization · en-de newstest 2014BLEU: 12.88 -> 4.81Hurt
add the SMT E-step between NMT training stepsOur Method · newstest 2014, four translation directionsBLEU: +2.06Helped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy