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Simultaneous Neural Machine Translation with Prefix Alignment
Simultaneous translation is a task that requires starting translation before the speaker has finished speaking, so we face a trade-off between latency and accuracy.
NAIST Simultaneous Speech-to-Text Translation System for IWSLT 2022
This paper describes NAIST’s simultaneous speech translation systems developed for IWSLT 2022 Evaluation Campaign.
NAIST English-to-Japanese Simultaneous Translation System for IWSLT 2021 Simultaneous Text-to-text Task
This paper describes NAIST’s system for the English-to-Japanese Simultaneous Text-to-text Translation Task in IWSLT 2021 Evaluation Campaign.
Average Token Delay: A Duration-aware Latency Metric for Simultaneous Translation
In this work, we propose a novel latency evaluation metric for simultaneous translation called \emph{Average Token Delay} (ATD) that focuses on the duration of partial translations.
Tagged End-to-End Simultaneous Speech Translation Training using Simultaneous Interpretation Data
In this paper, we propose an effective way to train a SimulST model using mixed data of SI and offline.
Average Token Delay: A Latency Metric for Simultaneous Translation
In this work, we propose a novel latency evaluation metric called Average Token Delay (ATD) that focuses on the end timings of partial translations in simultaneous translation.
Simultaneous Neural Machine Translation with Constituent Label Prediction
Simultaneous translation is a task in which translation begins before the speaker has finished speaking, so it is important to decide when to start the translation process.
