Simultaneous machine translation (SiMT) is a challenging task that requires
starting translation before the full source sentence is available.
Prefix-to-prefix framework is often applied to SiMT, which learns to predict
target tokens using only a partial source prefix. However, due to the word
order difference between languages, misaligned prefix pairs would make SiMT
models suffer from serious hallucination problems, i.e. target outputs that are
unfaithful to source inputs. Such problems can not only produce target tokens
that are not supported by the source prefix, but also hinder generating the
correct translation by receiving more source words. In this work, we propose a
Confidence-Based Simultaneous Machine Translation (CBSiMT) framework, which
uses model confidence to perceive hallucination tokens and mitigates their
negative impact with weighted prefix-to-prefix training. Specifically,
token-level and sentence-level weights are calculated based on model confidence
and acted on the loss function. We explicitly quantify the faithfulness of the
generated target tokens using the token-level weight, and employ the
sentence-level weight to alleviate the disturbance of sentence pairs with
serious word order differences on the model. Experimental results on MuST-C
English-to-Chinese and WMT15 German-to-English SiMT tasks demonstrate that our
method can consistently improve translation quality at most latency regimes,
with up to 2 BLEU scores improvement at low latency.

我们提出了一种基于置信度的同传机器翻译框架（CBSiMT），通过利用模型置信度来察觉幻觉标记，并通过加权的前缀对前缀训练来减轻它们的负面影响。实验结果表明我们的方法可以在大多数延迟范围内持续提高翻译质量，低延迟时提高 2 个 BLEU 分数。

CBSiMT: 基于加权前缀对前缀训练的同时翻译中减轻幻觉

CBSiMT: Mitigating Hallucination in Simultaneous Machine Translation  with Weighted Prefix-to-Prefix Training

Text-to-speech synthesis (TTS) has witnessed rapid progress in recent years,
where neural methods became capable of producing audios with high naturalness.
However, these efforts still suffer from two types of latencies: (a) the {\em
computational latency} (synthesizing time), which grows linearly with the
sentence length even with parallel approaches, and (b) the {\em input latency}
in scenarios where the input text is incrementally generated (such as in
simultaneous translation, dialog generation, and assistive technologies). To
reduce these latencies, we devise the first neural incremental TTS approach
based on the recently proposed prefix-to-prefix framework. We synthesize speech
in an online fashion, playing a segment of audio while generating the next,
resulting in an $O(1)$ rather than $O(n)$ latency.

利用前缀到前缀框架构建的神经增量文本转语音系统，实现了在线语音合成，从而将计算延迟和输入延迟分别降至 O (1) 水平。

基于前缀到前缀框架的增量式文本转语音合成

Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework

Simultaneous translation, which translates sentences before they are
finished, is useful in many scenarios but is notoriously difficult due to
word-order differences. While the conventional seq-to-seq framework is only
suitable for full-sentence translation, we propose a novel prefix-to-prefix
framework for simultaneous translation that implicitly learns to anticipate in
a single translation model. Within this framework, we present a very simple yet
surprisingly effective wait-k policy trained to generate the target sentence
concurrently with the source sentence, but always k words behind. Experiments
show our strategy achieves low latency and reasonable quality (compared to
full-sentence translation) on 4 directions: zh<->en and de<->en.

该研究提出了一种基于前缀的预测模型，采用 wait-k 策略来实现当前缀的翻译，并在中英文和德英文四个方向上实现了较低的延迟和合理的质量。