Current simultaneous speech translation models can process audio only up to a
few seconds long. Contemporary datasets provide an oracle segmentation into
sentences based on human-annotated transcripts and translations. However, the
segmentation into sentences is not available in the real world. Current speech
segmentation approaches either offer poor segmentation quality or have to trade
latency for quality. In this paper, we propose a novel segmentation approach
for a low-latency end-to-end speech translation. We leverage the existing
speech translation encoder-decoder architecture with ST CTC and show that it
can perform the segmentation task without supervision or additional parameters.
To the best of our knowledge, our method is the first that allows an actual
end-to-end simultaneous speech translation, as the same model is used for
translation and segmentation at the same time. On a diverse set of language
pairs and in- and out-of-domain data, we show that the proposed approach
achieves state-of-the-art quality at no additional computational cost.

提出一种新的分割方法，用于低延迟的端到端同时语音翻译，并且在多种语言对和领域数据中显示出具有先进水平的质量。

通过潜在对齐分段实现长篇连贯语音翻译

Long-Form End-to-End Speech Translation via Latent Alignment  Segmentation

This work addresses fair generative models. Dataset biases have been a major
cause of unfairness in deep generative models. Previous work had proposed to
augment large, biased datasets with small, unbiased reference datasets. Under
this setup, a weakly-supervised approach has been proposed, which achieves
state-of-the-art quality and fairness in generated samples. In our work, based
on this setup, we propose a simple yet effective approach. Specifically, first,
we propose fairTL, a transfer learning approach to learn fair generative
models. Under fairTL, we pre-train the generative model with the available
large, biased datasets and subsequently adapt the model using the small,
unbiased reference dataset. We find that our fairTL can learn expressive sample
generation during pre-training, thanks to the large (biased) dataset. This
knowledge is then transferred to the target model during adaptation, which also
learns to capture the underlying fair distribution of the small reference
dataset. Second, we propose fairTL++, where we introduce two additional
innovations to improve upon fairTL: (i) multiple feedback and (ii)
Linear-Probing followed by Fine-Tuning (LP-FT). Taking one step further, we
consider an alternative, challenging setup when only a pre-trained (potentially
biased) model is available but the dataset that was used to pre-train the model
is inaccessible. We demonstrate that our proposed fairTL and fairTL++ remain
very effective under this setup. We note that previous work requires access to
the large, biased datasets and is incapable of handling this more challenging
setup. Extensive experiments show that fairTL and fairTL++ achieve
state-of-the-art in both quality and fairness of generated samples. The code
and additional resources can be found at bearwithchris.github.io/fairTL/.

该研究提出了一种基于转移学习的公平生成模型方法，利用大规模含偏见、小规模不含偏见的数据集作为训练数据，在不同的模型中进行知识迁移，实现生成样本的高质量和公正性。

基于迁移学习的公平生成模型

Fair Generative Models via Transfer Learning

We consider the problem of translating, in an unsupervised manner, between
two domains where one contains some additional information compared to the
other. The proposed method disentangles the common and separate parts of these
domains and, through the generation of a mask, focuses the attention of the
underlying network to the desired augmentation alone, without wastefully
reconstructing the entire target. This enables state-of-the-art quality and
variety of content translation, as demonstrated through extensive quantitative
and qualitative evaluation. Our method is also capable of adding the separate
content of different guide images and domains as well as remove existing
separate content. Furthermore, our method enables weakly-supervised semantic
segmentation of the separate part of each domain, where only class labels are
provided. Our code is publicly available at
this https URL

本文提出了一种无监督的翻译方法，通过对领域进行分离和遮罩，实现了对目标部分的高效重建，从而实现了领域翻译的最新质量和多样性，并能识别不同领域的不同类别。