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Neural Machine Translation with Adequacy-Oriented Learning

AAAI 2019 · Reinforcement learning › RL from feedback · approach: RL for LLM reasoning

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the adequacy-oriented discriminator and CDR orientatorRNNSearch · Zh⇒En NIST test sets (All)BLEU: 36.76 -> 38.42Helped
Add discriminator and CDR orientator to the coverage modelRNNSearch-Coverage · Zh⇒En NIST test sets (All)BLEU: 39.13 -> 39.81Helped
Use the RNN discriminator and CDR orientatorRNNSearch · IWSLT 2014 De⇒EnBLEU: 26.51 -> 27.79Helped
Use the RNN discriminator and CDR orientatorTransformer-Base · WMT 2014 En⇒DeBLEU: 27.30 -> 28.01Helped
Use the RNN discriminator and CDR orientatorTransformer-Big · WMT 2014 En⇒DeBLEU: 28.35 -> 28.99Helped
Replace the CNN discriminator with the RNN discriminator (ablation)RNNSearch adversarial setup · Zh⇒En NIST test sets (All)BLEU: 37.54 vs. 37.59No clear effect
Use CDR reward instead of BLEU or chrF3 reward (ablation)RNNSearch orientator · Zh⇒En NIST test sets (All)BLEU: 37.44; 37.75; 38.02Helped
Add the discriminator to the CDR orientator (ablation)RNNSearch · Zh⇒En NIST test sets (All)BLEU: 38.02 -> 38.42Helped
Add the CDR orientator to the discriminator (ablation)RNNSearch · Zh⇒En NIST test sets (All)BLEU: 37.59 -> 38.42Helped
Add the CDR orientator without the discriminator (ablation)Transformer-Base · WMT 2014 En⇒DeBLEU: 27.30 -> 27.80Helped
Add the RNN discriminator to the CDR orientator (ablation)Transformer-Base · WMT 2014 En⇒DeBLEU: 27.80 -> 28.01Helped
Add the CDR orientator without the discriminator (ablation)Transformer-Big · WMT 2014 En⇒DeBLEU: 28.35 -> 28.63Helped

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