Measuring domain relevance of data and identifying or selecting well-fit
domain data for machine translation (MT) is a well-studied topic, but denoising
is not yet. Denoising is concerned with a different type of data quality and
tries to reduce the negative impact of data noise on MT training, in
particular, neural MT (NMT) training. This paper generalizes methods for
measuring and selecting data for domain MT and applies them to denoising NMT
training. The proposed approach uses trusted data and a denoising curriculum
realized by online data selection. Intrinsic and extrinsic evaluations of the
approach show its significant effectiveness for NMT to train on data with
severe noise.

本文章探讨如何使用在线数据选择的方式，通过使用受信任数据和去噪课程，来测量和去噪神经机器翻译（NMT）训练中的数据，并对提出的方法进行内在和外在评估，结果表明该方法在处理严重噪音的数据时具有显著的效果。

使用可信数据和在线数据选择去噪神经机器翻译训练

Denoising Neural Machine Translation Training with Trusted Data and  Online Data Selection

In this paper, we report on domain clustering in the ambit of an adaptive MT
architecture. A standard bottom-up hierarchical clustering algorithm has been
instantiated with five different distances, which have been compared, on an MT
benchmark built on 40 commercial domains, in terms of dendrograms, intrinsic
and extrinsic evaluations. The main outcome is that the most expensive distance
is also the only one able to allow the MT engine to guarantee good performance
even with few, but highly populated clusters of domains.

本文报告了关于自适应 MT 架构中的领域聚类。作者使用了五种不同的距离来进行标准的自底向上的层次聚类算法，针对 40 个商业领域构建了 MT 基准测试。作者比较了它们在树状图、内部和外部评价方面的优劣，主要结论是最昂贵的距离也是唯一一个能够让 MT 引擎在少量但高度聚集的域簇下保证良好性能的距离。