This work advances the field of sign language machine translation by focusing
on dataset quality and simplification of the translation system. We introduce
SignBank+, a clean version of the SignBank dataset, optimized for machine
translation. Contrary to previous works that employ complex factorization
techniques for translation, we advocate for a simplified text-to-text
translation approach. Our evaluation shows that models trained on SignBank+
surpass those on the original dataset, establishing a new benchmark and
providing an open resource for future research.

该研究通过改进数据集质量和翻译系统的简化，推动了手语机器翻译领域的发展。我们介绍了 SignBank+，这是 SignBank 数据集的精简版本，为机器翻译进行了优化。与以往利用复杂分解技术进行翻译的方法不同，我们提倡一种简化的文本对文本翻译方法。我们的评估表明，基于 SignBank + 训练的模型超过了原始数据集上的模型，建立了一个新的基准，并为未来的研究提供了一个开放的资源。

SignBank+: 多语种手语翻译数据集

SignBank+: Multilingual Sign Language Translation Dataset

Connectionist Temporal Classification (CTC) is a widely used approach for
automatic speech recognition (ASR) that performs conditionally independent
monotonic alignment. However for translation, CTC exhibits clear limitations
due to the contextual and non-monotonic nature of the task and thus lags behind
attentional decoder approaches in terms of translation quality. In this work,
we argue that CTC does in fact make sense for translation if applied in a joint
CTC/attention framework wherein CTC's core properties can counteract several
key weaknesses of pure-attention models during training and decoding. To
validate this conjecture, we modify the Hybrid CTC/Attention model originally
proposed for ASR to support text-to-text translation (MT) and speech-to-text
translation (ST). Our proposed joint CTC/attention models outperform
pure-attention baselines across six benchmark translation tasks.

这篇论文探讨了 Connectionist Temporal Classification 在翻译任务中的应用，并提出了 CTC/attention 的联合模型，改进了传统 attention 模型的训练表现和效果。

CTC 对齐提高自回归翻译

CTC Alignments Improve Autoregressive Translation

This paper describes the systems submitted to IWSLT 2021 by the Volctrans
team. We participate in the offline speech translation and text-to-text
simultaneous translation tracks. For offline speech translation, our best
end-to-end model achieves 8.1 BLEU improvements over the benchmark on the
MuST-C test set and is even approaching the results of a strong cascade
solution. For text-to-text simultaneous translation, we explore the best
practice to optimize the wait-k model. As a result, our final submitted systems
exceed the benchmark at around 7 BLEU on the same latency regime. We will
publish our code and model to facilitate both future research works and
industrial applications.
This paper describes the systems submitted to IWSLT 2021 by the Volctrans
team. We participate in the offline speech translation and text-to-text
simultaneous translation tracks. For offline speech translation, our best
end-to-end model achieves 7.9 BLEU improvements over the benchmark on the
MuST-C test set and is even approaching the results of a strong cascade
solution. For text-to-text simultaneous translation, we explore the best
practice to optimize the wait-k model. As a result, our final submitted systems
exceed the benchmark at around 7 BLEU on the same latency regime. We release
our code and model at
https://github.com/bytedance/neurst/tree/master/examples/iwslt21 to
facilitate both future research works and industrial applications.

该论文介绍了 Volctrans 团队在 IWSLT 2021 中提交的系统，包括离线语音翻译和文本 - 文本同时翻译。他们采用端到端模型进行离线语音翻译，在 MuST-C 测试集上比基准结果提高了 8.1 BLEU 分值，并接近强级联解决方案的结果。针对文本 - 文本同时翻译，他们探索了最优的 wait-k 模型实践，并且在相同的延迟范围内超过了基准结果约 7 BLEU 分值。他们已发布代码和模型以促进未来的研究工作和工业应用。