Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair
Yusuke Sakai, Mana Makinae, Hidetaka Kamigaito, Taro Watanabe
Abstract
In Simultaneous Machine Translation (SiMT), training with a simultaneous interpretation (SI) corpus is an effective method for achieving high-quality yet low-latency. However, constructing such a corpus is challenging due to high costs, and limitations in annotator capabilities, and as a result, existing SI corpora are limited. Therefore, we propose a method to convert existing speech translation (ST) corpora into interpretation-style corpora, 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 using the LLM-SI-Corpus reduces latency while achieving better quality compared to models fine-tuned with other corpora in both speech-to-text and text-to-text settings. The LLM-SI-Corpus is available at https://github.com/yusuke1997/LLM-SI-Corpus.
BibTeX
@inproceedings{sakai-etal-2024-simultaneous,
title = "Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair",
author = "Sakai, Yusuke and
Makinae, Mana and
Kamigaito, Hidetaka and
Watanabe, Taro",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1248/",
doi = "10.18653/v1/2024.emnlp-main.1248",
pages = "22375--22398"
}