Popular Neural Machine Translation model training uses strategies like
backtranslation to improve BLEU scores, requiring large amounts of additional
data and training. We introduce a class of conditional
generative-discriminative hybrid losses that we use to fine-tune a trained
machine translation model. Through a combination of targeted fine-tuning
objectives and intuitive re-use of the training data the model has failed to
adequately learn from, we improve the model performance of both a
sentence-level and a contextual model without using any additional data. We
target the improvement of pronoun translations through our fine-tuning and
evaluate our models on a pronoun benchmark testset. Our sentence-level model
shows a 0.5 BLEU improvement on both the WMT14 and the IWSLT13 De-En testsets,
while our contextual model achieves the best results, improving from 31.81 to
32 BLEU on WMT14 De-En testset, and from 32.10 to 33.13 on the IWSLT13 De-En
testset, with corresponding improvements in pronoun translation. We further
show the generalizability of our method by reproducing the improvements on two
additional language pairs, Fr-En and Cs-En. Code available at
<this https URL>.

本研究引入了一类新的条件生成 - 判别混合 Loss，用于微调经过训练的机器翻译模型，通过有针对性的微调目标和直观地重复利用训练数据，从而在不使用任何其他数据的情况下提高了模型的性能。我们针对代词翻译问题进行微调，并在代词基准测试集上对模型进行评估。结果表明我们的模型在几个测试集上都实现了较高的 BLEU 分数，特别是在 IWSLT13 De-En testset 上达到了最佳效果。同时我们还在 Fr-En 和 Cs-En 上重现了该方法的改进效果。