论文标题
重新介绍非自动回旋机器翻译的参考
Rephrasing the Reference for Non-Autoregressive Machine Translation
论文作者
论文摘要
非自动入学神经机器翻译（NAT）模型遇到了多模式问题，即可能存在多种模式的源句子，因此当NAT输出更接近其他翻译时，参考句子可能不适合训练。为了应对这个问题，我们引入了一个改造器，以根据NAT输出来重新分配参考句子为NAT提供更好的训练目标。当我们基于Rephraser输出而不是参考句子训练NAT时，Rephraser输出应与NAT输出非常吻合，并且不会偏离参考，可以将其量化为奖励功能并通过增强学习来优化。关于主要WMT基准和NAT基准的实验表明，我们的方法始终提高NAT的翻译质量。具体而言，我们最佳的变体与自回归变压器的性能相当，而推理效率高14.7倍。
Non-autoregressive neural machine translation (NAT) models suffer from the multi-modality problem that there may exist multiple possible translations of a source sentence, so the reference sentence may be inappropriate for the training when the NAT output is closer to other translations. In response to this problem, we introduce a rephraser to provide a better training target for NAT by rephrasing the reference sentence according to the NAT output. As we train NAT based on the rephraser output rather than the reference sentence, the rephraser output should fit well with the NAT output and not deviate too far from the reference, which can be quantified as reward functions and optimized by reinforcement learning. Experiments on major WMT benchmarks and NAT baselines show that our approach consistently improves the translation quality of NAT. Specifically, our best variant achieves comparable performance to the autoregressive Transformer, while being 14.7 times more efficient in inference.
