In Simultaneous Machine Translation (SiMT) systems, training with a simultaneous interpretation (SI) corpus is an effective method for achieving high-quality yet low-latency systems. However, it is very challenging to curate such a corpus due to limitations in the abilities of annotators, and hence, existing SI corpora are limited. Therefore, we propose a method to convert existing speech translation corpora into interpretation-style data, maintaining the original word order and preserving the entire source content using Large Language Models (LLM-SI-Corpus). We demonstrate that fine-tuning SiMT models in text-to-text and speech-to-text settings with the LLM-SI-Corpus reduces latencies while maintaining the same level of quality as the models trained with offline datasets. The LLM-SI-Corpus is available at \url{https://github.com/yusuke1997/LLM-SI-Corpus}.
@article{arxiv.2404.12299,
title = {Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair},
author = {Yusuke Sakai and Mana Makinae and Hidetaka Kamigaito and Taro Watanabe},
journal= {arXiv preprint arXiv:2404.12299},
year = {2024}
}