Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair
<|reference_start|>Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair: 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}.<|reference_end|>
@article{sakai2024simultaneous,
    title={Simultaneous Interpretation Corpus Construction by Large Language Models
  in Distant Language Pair},
    author={Yusuke Sakai, Mana Makinae, Hidetaka Kamigaito, Taro Watanabe},
    journal={arXiv preprint arXiv:2404.12299},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12299},
    primaryClass={cs.CL cs.AI cs.LG cs.SD eess.AS}
}