论文标题
高效但有竞争力的语音翻译：fbk@iwslt2022
Efficient yet Competitive Speech Translation: FBK@IWSLT2022
论文作者
论文摘要
该FBK系统提交IWSLT 2022离线和同时进行语音翻译任务的主要目标是降低模型培训成本而不牺牲翻译质量。因此，我们首先要质疑ASR预培训的需求，这表明这对于获得竞争成果并不是必不可少的。其次，我们专注于数据过滤，表明一种简单的方法，该方法着眼于源和目标字符之间的比率可产生1个BLEU的质量提高。第三，我们比较不同的方法，以减少在句子级别手动分割的训练数据与自动分割的推理数据之间的音频分割不匹配的有害效果。为了降低培训成本的同一目标，我们通过对离线ST进行培训的相同模型同时参加了同时任务。我们的轻量级训练策略的有效性由在去年的获胜系统中的IWSLT2020测试集对IWSLT2020测试集的1.6 BLEU改进中，在高资源数据条件下获得了1.6 BLEU，在高资源数据条件下获得了高分分数。
The primary goal of this FBK's systems submission to the IWSLT 2022 offline and simultaneous speech translation tasks is to reduce model training costs without sacrificing translation quality. As such, we first question the need of ASR pre-training, showing that it is not essential to achieve competitive results. Second, we focus on data filtering, showing that a simple method that looks at the ratio between source and target characters yields a quality improvement of 1 BLEU. Third, we compare different methods to reduce the detrimental effect of the audio segmentation mismatch between training data manually segmented at sentence level and inference data that is automatically segmented. Towards the same goal of training cost reduction, we participate in the simultaneous task with the same model trained for offline ST. The effectiveness of our lightweight training strategy is shown by the high score obtained on the MuST-C en-de corpus (26.7 BLEU) and is confirmed in high-resource data conditions by a 1.6 BLEU improvement on the IWSLT2020 test set over last year's winning system.
