Papers with end-to-end neural machine translation

2 papers
Adaptive Multi-pass Decoder for Neural Machine Translation (D18-1)

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Challenge: End-to-end neural machine translation (NMT) has attracted increasing attention in recent years.
Approach: They propose an adaptive multi-pass decoder which introduces a flexible multi- pass polishing mechanism to extend the capacity of NMT via reinforcement learning.
Outcome: The proposed architecture improves Chinese-English translation with 1.55 BLEU . the proposed architecture adopts a flexible multi-pass polishing mechanism .
PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation (2022.coling-1)

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Challenge: Existing methods for adversarial example generation are word-level or character-level, which ignore the ubiquitous phrase structure.
Approach: They propose a phrase-level adversarial example generation framework to enhance the robustness of the translation model by adopting a sentence-level substitution strategy.
Outcome: The proposed method improves translation performance and robustness to noise on three benchmarks.

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