This paper proposes a method for improved CTC inference with searched
intermediates and multi-pass conditioning. The paper first formulates
self-conditioned CTC as a probabilistic model with an intermediate prediction
as a latent representation and provides a tractable conditioning framework. We
then propose two new conditioning methods based on the new formulation: (1)
Searched intermediate conditioning that refines intermediate predictions with
beam-search, (2) Multi-pass conditioning that uses predictions of previous
inference for conditioning the next inference. These new approaches enable
better conditioning than the original self-conditioned CTC during inference and
improve the final performance. Experiments with the LibriSpeech dataset show
relative 3%/12% performance improvement at the maximum in test clean/other sets
compared to the original self-conditioned CTC.

本文提出了一种使用搜索的中间结果和多次条件的改进 CTC 推断方法，通过提出新的条件方法，如搜索中间结果和多次通过条件，使得 CTC 推断中的条件能力更强，相对于原本的自我条件的 CTC，在 LibriSpeech 数据集上测试表现有明显提高。

优化中间结果提升 CTC 推导效果

Better Intermediates Improve CTC Inference

Translating in real-time, a.k.a. simultaneous translation, outputs
translation words before the input sentence ends, which is a challenging
problem for conventional machine translation methods. We propose a neural
machine translation (NMT) framework for simultaneous translation in which an
agent learns to make decisions on when to translate from the interaction with a
pre-trained NMT environment. To trade off quality and delay, we extensively
explore various targets for delay and design a method for beam-search
applicable in the simultaneous MT setting. Experiments against state-of-the-art
baselines on two language pairs demonstrate the efficacy of the proposed
framework both quantitatively and qualitatively.

本论文提出了一种基于神经机器翻译的实时翻译框架，通过代理决策来实现翻译输出实时化并且具有可控的翻译质量与延迟，通过在两种语言对上的实验结果表明了该方法在定量与定性方面的有效性。