# Direct Models for Simultaneous Translation and Automatic Subtitling: FBK@IWSLT2023 Source: [Direct Models for Simultaneous Translation and Automatic Subtitling: FBK@IWSLT2023]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:52:52.049Z Authors: Sara Papi, Marco Gaido, Matteo Negri Published: 2023-09-27T10:24:42.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Automatic Subtitling on IWSLT en-es 2023 (val)](): Automatic Subtitling; IWSLT en-es 2023 (val); SubER (TED cased): 52.5. - [Automatic Subtitling on IWSLT en-de 2023 (val)](): Automatic Subtitling; IWSLT en-de 2023 (val); SubER (TED uncased): not recorded. ## Original sources and implementation [Original paper]() [PDF]()