We frame the task of machine translation evaluation as one of scoring machine
translation output with a sequence-to-sequence paraphraser, conditioned on a
human reference. We propose training the paraphraser as a multilingual NMT
system, treating paraphrasing as a zero-shot translation task (e.g., Czech to
Czech). This results in the paraphraser's output mode being centered around a
copy of the input sequence, which represents the best case scenario where the
MT system output matches a human reference. Our method is simple and intuitive,
and does not require human judgements for training. Our single model (trained
in 39 languages) outperforms or statistically ties with all prior metrics on
the WMT 2019 segment-level shared metrics task in all languages (excluding
Gujarati where the model had no training data). We also explore using our model
for the task of quality estimation as a metric--conditioning on the source
instead of the reference--and find that it significantly outperforms every
submission to the WMT 2019 shared task on quality estimation in every language
pair.

使用序列到序列的释义器作为人类参考的评估工具，将机器翻译评估任务转化为对机器翻译输出进行评分的任务。通过将释义技术视为零射任务，将释义器训练为多语言 NMT 系统，并将其应用于质量估计任务中，以条件化源，而不是参考，发现其在各种语言对中的表现均优于 WMT 2019 质量评估任务中的所有其他度量标准。

通过零样式改写实现多语言自动机器翻译评估

Automatic Machine Translation Evaluation in Many Languages via Zero-Shot  Paraphrasing

We propose a method to transfer knowledge across neural machine translation
(NMT) models by means of a shared dynamic vocabulary. Our approach allows to
extend an initial model for a given language pair to cover new languages by
adapting its vocabulary as long as new data become available (i.e., introducing
new vocabulary items if they are not included in the initial model). The
parameter transfer mechanism is evaluated in two scenarios: i) to adapt a
trained single language NMT system to work with a new language pair and ii) to
continuously add new language pairs to grow to a multilingual NMT system. In
both the scenarios our goal is to improve the translation performance, while
minimizing the training convergence time. Preliminary experiments spanning five
languages with different training data sizes (i.e., 5k and 50k parallel
sentences) show a significant performance gain ranging from +3.85 up to +13.63
BLEU in different language directions. Moreover, when compared with training an
NMT model from scratch, our transfer-learning approach allows us to reach
higher performance after training up to 4% of the total training steps.

本研究提出了一种神经机器翻译（NMT）模型间的知识转移方法，通过共享动态词汇表来适应新语言，以达到提高翻译性能的目的，并在多种语言方向上进行了实验，结果表明具有良好的性能。