Neural machine translation (NMT) is sensitive to domain shift. In this paper,
we address this problem in an active learning setting where we can spend a
given budget on translating in-domain data, and gradually fine-tune a
pre-trained out-of-domain NMT model on the newly translated data. Existing
active learning methods for NMT usually select sentences based on uncertainty
scores, but these methods require costly translation of full sentences even
when only one or two key phrases within the sentence are informative. To
address this limitation, we re-examine previous work from the phrase-based
machine translation (PBMT) era that selected not full sentences, but rather
individual phrases. However, while incorporating these phrases into PBMT
systems was relatively simple, it is less trivial for NMT systems, which need
to be trained on full sequences to capture larger structural properties of
sentences unique to the new domain. To overcome these hurdles, we propose to
select both full sentences and individual phrases from unlabelled data in the
new domain for routing to human translators. In a German-English translation
task, our active learning approach achieves consistent improvements over
uncertainty-based sentence selection methods, improving up to 1.2 BLEU score
over strong active learning baselines.

本文拟通过选择在新领域非标记数据中的全句和短语，将其路由至人类翻译，解决神经机器翻译在领域转移方面的限制，从而在德语 - 英语翻译任务上，相比于不确定性句子选择方法，实现了一定程度的改进。

神经机器翻译的短语级主动学习

Phrase-level Active Learning for Neural Machine Translation

We aim to shed light on the strengths and weaknesses of the newly introduced
neural machine translation paradigm. To that end, we conduct a multifaceted
evaluation in which we compare outputs produced by state-of-the-art neural
machine translation and phrase-based machine translation systems for 9 language
directions across a number of dimensions. Specifically, we measure the
similarity of the outputs, their fluency and amount of reordering, the effect
of sentence length and performance across different error categories. We find
out that translations produced by neural machine translation systems are
considerably different, more fluent and more accurate in terms of word order
compared to those produced by phrase-based systems. Neural machine translation
systems are also more accurate at producing inflected forms, but they perform
poorly when translating very long sentences.

本文评估了新兴的神经机器翻译系统和基于短语的机器翻译系统在九种语言方向上的表现，发现相对于基于短语的系统，神经机器翻译系统翻译更为流畅和准确，但在翻译超长句子时表现差。

神经机器翻译与基于短语的机器翻译在 9 个语言方向上的多方面评估

A Multifaceted Evaluation of Neural versus Phrase-Based Machine  Translation for 9 Language Directions

Recently, the development of neural machine translation (NMT) has
significantly improved the translation quality of automatic machine
translation. While most sentences are more accurate and fluent than
translations by statistical machine translation (SMT)-based systems, in some
cases, the NMT system produces translations that have a completely different
meaning. This is especially the case when rare words occur.
When using statistical machine translation, it has already been shown that
significant gains can be achieved by simplifying the input in a preprocessing
step. A commonly used example is the pre-reordering approach.
In this work, we used phrase-based machine translation to pre-translate the
input into the target language. Then a neural machine translation system
generates the final hypothesis using the pre-translation. Thereby, we use
either only the output of the phrase-based machine translation (PBMT) system or
a combination of the PBMT output and the source sentence.
We evaluate the technique on the English to German translation task. Using
this approach we are able to outperform the PBMT system as well as the baseline
neural MT system by up to 2 BLEU points. We analyzed the influence of the
quality of the initial system on the final result.

使用词汇短语转换（phrase-based machine translation）方法预处理输入，进而提升神经机器翻译（NMT）的翻译质量，在英德翻译任务中，使用该方法的性能跃升 2 个 BLEU 分数，同时分析了初始系统质量对最终结果的影响。