论文标题
IWSLT 2020的端到端和同时语音翻译挑战任务的贸易联盟
ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020
论文作者
论文摘要
本文介绍了针对IWSLT 2020评估活动中的两个挑战赛道开发的贸易联盟翻译系统，即离线语音翻译和同时的语音翻译。 Trac联盟由来自法国三个学术实验室的研究人员组成：Lia（AvignonUniversité），Lig（Lig（UniversitéGrenobleAlpes）和Lium（Le MansUniversité）。基于注意的编码器模型，训练有素的端到端，用于我们的脱机语音翻译轨道。我们的贡献集中在数据增强和多个模型的结合上。在同时的语音翻译曲目中，我们基于基于变压器的Wait-K模型，用于文本到文本子任务。对于语音到文本的同时翻译，我们将Wait-K MT系统附加到混合ASR系统。我们提出了一种算法来控制ASR+MT级联的潜伏期，并在两个子任务上实现了良好的延迟质量折衷。
This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline speech translation and simultaneous speech translation. ON-TRAC Consortium is composed of researchers from three French academic laboratories: LIA (Avignon Université), LIG (Université Grenoble Alpes), and LIUM (Le Mans Université). Attention-based encoder-decoder models, trained end-to-end, were used for our submissions to the offline speech translation track. Our contributions focused on data augmentation and ensembling of multiple models. In the simultaneous speech translation track, we build on Transformer-based wait-k models for the text-to-text subtask. For speech-to-text simultaneous translation, we attach a wait-k MT system to a hybrid ASR system. We propose an algorithm to control the latency of the ASR+MT cascade and achieve a good latency-quality trade-off on both subtasks.
