Data augmentation is an effective way to diversify corpora in machine
translation, but previous methods may introduce semantic inconsistency between
original and augmented data because of irreversible operations and random
subword sampling procedures. To generate both symbolically diverse and
semantically consistent augmentation data, we propose Deterministic Reversible
Data Augmentation (DRDA), a simple but effective data augmentation method for
neural machine translation. DRDA adopts deterministic segmentations and
reversible operations to generate multi-granularity subword representations and
pulls them closer together with multi-view techniques. With no extra corpora or
model changes required, DRDA outperforms strong baselines on several
translation tasks with a clear margin (up to 4.3 BLEU gain over Transformer)
and exhibits good robustness in noisy, low-resource, and cross-domain datasets.

确定性可逆数据增强（DRDA）是一种简单但有效的数据增强方法，用于神经机器翻译，通过采用确定性分段和可逆操作生成多粒度子词表示，并通过多视角技术将它们拉近，无需额外的语料库或模型变更，DRDA 在几个翻译任务中优于强基线模型（Transformer），在嘈杂、资源匮乏和跨领域数据集中表现出良好的鲁棒性，达到多达 4.3 BLEU 增益。

神经机器翻译的确定性可逆数据增强

Deterministic Reversible Data Augmentation for Neural Machine  Translation

Direct speech-to-speech translation (S2ST) models suffer from data scarcity
issues as there exists little parallel S2ST data, compared to the amount of
data available for conventional cascaded systems that consist of automatic
speech recognition (ASR), machine translation (MT), and text-to-speech (TTS)
synthesis. In this work, we explore self-supervised pre-training with unlabeled
speech data and data augmentation to tackle this issue. We take advantage of a
recently proposed speech-to-unit translation (S2UT) framework that encodes
target speech into discrete representations, and transfer pre-training and
efficient partial finetuning techniques that work well for speech-to-text
translation (S2T) to the S2UT domain by studying both speech encoder and
discrete unit decoder pre-training. Our experiments on Spanish-English
translation show that self-supervised pre-training consistently improves model
performance compared with multitask learning with an average 6.6-12.1 BLEU
gain, and it can be further combined with data augmentation techniques that
apply MT to create weakly supervised training data. Audio samples are available
at:
this https URL .

本文讨论了直接语音到语音翻译模型面临的数据稀缺性问题，探索了自监督预训练和数据增强技术来解决此问题，实验表明自监督预训练可以提高模型性能，结合数据增强技术可以进一步提高模型性能。

使用自监督预训练和数据增强增强的直接语音翻译

Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation

We present a textless speech-to-speech translation (S2ST) system that can
translate speech from one language into another language and can be built
without the need of any text data. Different from existing work in the
literature, we tackle the challenge in modeling multi-speaker target speech and
train the systems with real-world S2ST data. The key to our approach is a
self-supervised unit-based speech normalization technique, which finetunes a
pre-trained speech encoder with paired audios from multiple speakers and a
single reference speaker to reduce the variations due to accents, while
preserving the lexical content. With only 10 minutes of paired data for speech
normalization, we obtain on average 3.2 BLEU gain when training the S2ST model
on the VoxPopuli S2ST dataset, compared to a baseline trained on un-normalized
speech target. We also incorporate automatically mined S2ST data and show an
additional 2.0 BLEU gain. To our knowledge, we are the first to establish a
textless S2ST technique that can be trained with real-world data and works for
multiple language pairs. Audio samples are available at
this https URL .

我们提出了一种无需文本数据即可构建的无文本语音到语音翻译系统，采用了自监督单元级别的语音标准化技术来优化多说话者语音的模型，仅使用了 10 分钟的数据训练该技术，可在 VoxPopuli S2ST 数据集上实现平均 3.2 BLEU 分数的增益，是首次建立了可用于多种语言对的无文本 S2ST 技术。