  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.
