Neural machine translation (NMT) models are usually trained with the
word-level loss using the teacher forcing algorithm, which not only evaluates
the translation improperly but also suffers from exposure bias. Sequence-level
training under the reinforcement framework can mitigate the problems of the
word-level loss, but its performance is unstable due to the high variance of
the gradient estimation. On these grounds, we present a method with a
differentiable sequence-level training objective based on probabilistic n-gram
matching which can avoid the reinforcement framework. In addition, this method
performs greedy search in the training which uses the predicted words as
context just as at inference to alleviate the problem of exposure bias.
Experiment results on the NIST Chinese-to-English translation tasks show that
our method significantly outperforms the reinforcement-based algorithms and
achieves an improvement of 1.5 BLEU points on average over a strong baseline
system.

该研究提出一种基于可微序列级训练目标的方法，使用概率 n-gram 匹配来避免强化学习框架，该方法在训练中执行贪心搜索并使用预测的单词作为上下文，以缓解曝光偏差问题，实验结果表明，该方法在 NIST 中英文翻译任务中显着优于基于强化学习的算法，并在强基线系统上平均实现了 1.5 个 BLEU 点的改进。

基于概率 N-gram 匹配的贪婪搜索神经机器翻译

Greedy Search with Probabilistic N-gram Matching for Neural Machine  Translation

The Teacher Forcing algorithm trains recurrent networks by supplying observed
sequence values as inputs during training and using the network's own
one-step-ahead predictions to do multi-step sampling. We introduce the
Professor Forcing algorithm, which uses adversarial domain adaptation to
encourage the dynamics of the recurrent network to be the same when training
the network and when sampling from the network over multiple time steps. We
apply Professor Forcing to language modeling, vocal synthesis on raw waveforms,
handwriting generation, and image generation. Empirically we find that
Professor Forcing acts as a regularizer, improving test likelihood on character
level Penn Treebank and sequential MNIST. We also find that the model
qualitatively improves samples, especially when sampling for a large number of
time steps. This is supported by human evaluation of sample quality. Trade-offs
between Professor Forcing and Scheduled Sampling are discussed. We produce
T-SNEs showing that Professor Forcing successfully makes the dynamics of the
network during training and sampling more similar.

本文介绍了教师强制算法和教授强制算法，采用对抗性域适应，使得递归网络在训练和多阶段抽样时的动态相同，并应用于语言模型、原始波形的声音合成、手写生成和图像生成。实验证明，本文方法在字符级 Penn Treebank 和连续 MNIST 的测试似然度上有所提升，并且样本的质量在高时间步长的采样时有所改善。