This paper describes the FBK's participation in the Simultaneous Translation and Automatic Subtitling tracks of the IWSLT 2023 Evaluation Campaign. Our submission focused on the use of direct architectures to perform both tasks: for the simultaneous one, we leveraged the knowledge already acquired by offline-trained models and directly applied a policy to obtain the real-time inference; for the subtitling one, we adapted the direct ST model to produce well-formed subtitles and exploited the same architecture to produce timestamps needed for the subtitle synchronization with audiovisual content. Our English-German SimulST system shows a reduced computational-aware latency compared to the one achieved by the top-ranked systems in the 2021 and 2022 rounds of the task, with gains of up to 3.5 BLEU. Our automatic subtitling system outperforms the only existing solution based on a direct system by 3.7 and 1.7 SubER in English-German and English-Spanish respectively.

翻译：本文描述了FBK参与IWSLT 2023评估活动的同传翻译与自动字幕生成赛道的工作。我们的提交方案聚焦于利用直接架构执行两项任务：在同传任务中，我们利用离线训练模型已获取的知识，直接应用策略实现实时推理；在字幕生成任务中，我们将直接语音翻译模型适配为生成格式规范的字幕，并利用相同架构生成与音视频内容同步所需的时间戳。我们的英德同传系统相较2021和2022年度任务中排名最高的系统，在计算感知延迟上有所降低，且BLEU值最高提升3.5分。在英德和英西方向中，我们的自动字幕生成系统分别以3.7和1.7的SubER指标超越了现有唯一的基于直接系统的解决方案。