We adapt the well-known beam-search algorithm for machine translation to
operate in a cascaded real-time speech translation system. This proved to be
more complex than initially anticipated, due to four key challenges: (1)
real-time processing of intermediate and final transcriptions with incomplete
words from ASR, (2) emitting intermediate and final translations with minimal
user perceived latency, (3) handling beam search hypotheses that have unequal
length and different model state, and (4) handling sentence boundaries.
Previous work in the field of simultaneous machine translation only implemented
greedy decoding. We present a beam-search realization that handles all of the
above, providing guidance through the minefield of challenges. Our approach
increases the BLEU score by 1 point compared to greedy search, reduces the CPU
time by up to 40% and character flicker rate by 20+% compared to a baseline
heuristic that just retranslates input repeatedly.

我们将广泛应用的波束搜索算法用于级联实时语音翻译系统，通过解决实时处理中的四个关键挑战以及提供比贪婪搜索更好的效果，如处理不完整的识别结果，减少用户感知延迟，处理不同长度和模型状态的波束搜索假设，处理句子边界，我们的方法将 BLEU 分数提升 1 点，将 CPU 时间减少 40%，字符闪烁率降低 20% 以上。

在级联流式语音翻译中导航机器翻译束搜索的雷区

Navigating the Minefield of MT Beam Search in Cascaded Streaming Speech  Translation

Semantic parsing solves knowledge base (KB) question answering (KBQA) by
composing a KB query, which generally involves node extraction (NE) and graph
composition (GC) to detect and connect related nodes in a query. Despite the
strong causal effects between NE and GC, previous works fail to directly model
such causalities in their pipeline, hindering the learning of subtask
correlations. Also, the sequence-generation process for GC in previous works
induces ambiguity and exposure bias, which further harms accuracy. In this
work, we formalize semantic parsing into two stages. In the first stage (graph
structure generation), we propose a causal-enhanced table-filler to overcome
the issues in sequence-modelling and to learn the internal causalities. In the
second stage (relation extraction), an efficient beam-search algorithm is
presented to scale complex queries on large-scale KBs. Experiments on LC-QuAD
1.0 indicate that our method surpasses previous state-of-the-arts by a large
margin (17%) while remaining time and space efficiency. The code and models are
available at this https URL.

本研究提出一种基于图结构生成和关系抽取的语义解析模型，其中使用因果关系增强表填充器和高效的波束搜索算法解决了之前模型中出现的序列生成模糊和暴露偏见问题，并在大规模知识库上具有显著的优势。