Translated texts exhibit systematic linguistic differences compared to
original texts in the same language, and these differences are referred to as
translationese. Translationese has effects on various cross-lingual natural
language processing tasks, potentially leading to biased results. In this
paper, we explore a novel approach to reduce translationese in translated
texts: translation-based style transfer. As there are no parallel
human-translated and original data in the same language, we use a
self-supervised approach that can learn from comparable (rather than parallel)
mono-lingual original and translated data. However, even this self-supervised
approach requires some parallel data for validation. We show how we can
eliminate the need for parallel validation data by combining the
self-supervised loss with an unsupervised loss. This unsupervised loss
leverages the original language model loss over the style-transferred output
and a semantic similarity loss between the input and style-transferred output.
We evaluate our approach in terms of original vs. translationese binary
classification in addition to measuring content preservation and target-style
fluency. The results show that our approach is able to reduce translationese
classifier accuracy to a level of a random classifier after style transfer
while adequately preserving the content and fluency in the target original
style.

研究采用自监督学习与非监督学习相结合的方法，通过基于翻译的风格转换降低翻译文本中的翻译风格，消除了对平行验证数据的需求，有效地降低了翻译类文本的分类准确性，并在目标原始风格中保持了内容和流畅度。

无需平行数据的翻译去 “翻译式” 重叠

Translating away Translationese without Parallel Data

Cross-lingual natural language processing relies on translation, either by
humans or machines, at different levels, from translating training data to
translating test sets. However, compared to original texts in the same
language, translations possess distinct qualities referred to as
translationese. Previous research has shown that these translation artifacts
influence the performance of a variety of cross-lingual tasks. In this work, we
propose a novel approach to reducing translationese by extending an established
bias-removal technique. We use the Iterative Null-space Projection (INLP)
algorithm, and show by measuring classification accuracy before and after
debiasing, that translationese is reduced at both sentence and word level. We
evaluate the utility of debiasing translationese on a natural language
inference (NLI) task, and show that by reducing this bias, NLI accuracy
improves. To the best of our knowledge, this is the first study to debias
translationese as represented in latent embedding space.

本研究提出了一种新的解决跨语言自然语言处理中翻译语言特征（translationese）影响的方法，并且使用 Iterative Null-space Projection（INLP）算法将翻译语言特征降低到了句子和单词级别，通过评估自然语言推理（NLI）任务的效果发现，降低这种偏差可以改善 NLI 的准确率，这是首次在潜在状态空间中对 translationese 进行去偏置的研究。

消除翻译偏见的努力

Towards Debiasing Translation Artifacts

This paper develops automatic song translation (AST) for tonal languages and
addresses the unique challenge of aligning words' tones with melody of a song
in addition to conveying the original meaning. We propose three criteria for
effective AST -- preserving meaning, singability and intelligibility -- and
design metrics for these criteria. We develop a new benchmark for
English--Mandarin song translation and develop an unsupervised AST system,
Guided AliGnment for Automatic Song Translation (GagaST), which combines
pre-training with three decoding constraints. Both automatic and human
evaluations show GagaST successfully balances semantics and singability.

本文提出了一种用于声调语言的自动歌曲翻译系统，解决了将单词的音高与歌曲旋律对齐并传达原意的独特挑战。该系统结合预训练和三种解码约束，成功实现了语义和歌唱性的平衡。