When the amount of parallel sentences available to train a neural machine
translation is scarce, a common practice is to generate new synthetic training
samples from them. A number of approaches have been proposed to produce
synthetic parallel sentences that are similar to those in the parallel data
available. These approaches work under the assumption that non-fluent
target-side synthetic training samples can be harmful and may deteriorate
translation performance. Even so, in this paper we demonstrate that synthetic
training samples with non-fluent target sentences can improve translation
performance if they are used in a multilingual machine translation framework as
if they were sentences in another language. We conducted experiments on ten
low-resource and four high-resource translation tasks and found out that this
simple approach consistently improves translation performance as compared to
state-of-the-art methods for generating synthetic training samples similar to
those found in corpora. Furthermore, this improvement is independent of the
size of the original training corpus, the resulting systems are much more
robust against domain shift and produce less hallucinations.

从有限的平行语料中生成合成训练样本，即非流利目标端句子能够在多语种机器翻译框架中有效地提高翻译性能，并且这种方法对原始训练语料的规模不敏感，从而使系统更鲁棒、产生更少的幻觉。

非流利的合成目标语言数据提高神经机器翻译

Non-Fluent Synthetic Target-Language Data Improve Neural Machine  Translation

Recent works of opinion expression identification (OEI) rely heavily on the
quality and scale of the manually-constructed training corpus, which could be
extremely difficult to satisfy. Crowdsourcing is one practical solution for
this problem, aiming to create a large-scale but quality-unguaranteed corpus.
In this work, we investigate Chinese OEI with extremely-noisy crowdsourcing
annotations, constructing a dataset at a very low cost. Following zhang et al.
(2021), we train the annotator-adapter model by regarding all annotations as
gold-standard in terms of crowd annotators, and test the model by using a
synthetic expert, which is a mixture of all annotators. As this
annotator-mixture for testing is never modeled explicitly in the training
phase, we propose to generate synthetic training samples by a pertinent mixup
strategy to make the training and testing highly consistent. The simulation
experiments on our constructed dataset show that crowdsourcing is highly
promising for OEI, and our proposed annotator-mixup can further enhance the
crowdsourcing modeling.

本研究探讨了如何使用极其嘈杂的众包注释构建一个低成本的数据集，并通过注释器适配器模型和相关混合策略，提高了众包建模的准确性，结果表明众包在观点表达识别方面有很高的应用前景。