Recent years have seen remarkable advances in the field of Simultaneous
Machine Translation (SiMT) due to the introduction of innovative policies that
dictate whether to READ or WRITE at each step of the translation process.
However, a common assumption in many existing studies is that operations are
carried out at the subword level, even though the standard unit for input and
output in most practical scenarios is typically at the word level. This paper
demonstrates that policies devised and validated at the subword level are
surpassed by those operating at the word level, which process multiple subwords
to form a complete word in a single step. Additionally, we suggest a method to
boost SiMT models using language models (LMs), wherein the proposed word-level
policy plays a vital role in addressing the subword disparity between LMs and
SiMT models. Code is available at this https URL

研究表明，在同时机器翻译中，使用词级操作的策略优于子词级操作的策略，并且提出了一种使用语言模型提升翻译模型的方法。

基于词级策略的增强式同时机器翻译

Enhanced Simultaneous Machine Translation with Word-level Policies

The recognition of personalized content, such as contact names, remains a
challenging problem for end-to-end speech recognition systems. In this work, we
demonstrate how first and second-pass rescoring strategies can be leveraged
together to improve the recognition of such words. Following previous work, we
use a shallow fusion approach to bias towards recognition of personalized
content in the first-pass decoding. We show that such an approach can improve
personalized content recognition by up to 16% with minimum degradation on the
general use case. We describe a fast and scalable algorithm that enables our
biasing models to remain at the word-level, while applying the biasing at the
subword level. This has the advantage of not requiring the biasing models to be
dependent on any subword symbol table. We also describe a novel second-pass
de-biasing approach: used in conjunction with a first-pass shallow fusion that
optimizes on oracle WER, we can achieve an additional 14% improvement on
personalized content recognition, and even improve accuracy for the general use
case by up to 2.5%.

本文提出一种新的基于第一、第二次遍历的重评分策略，以及浅层融合的方法来解决个人化内容识别在端对端语音识别系统中的挑战，并展示了在子词级别上实现这种偏向，大大提高了个性化内容识别的准确性，同时保证了一般用例下识别准确度的最小降低。