Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909174584049664 |
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| author | Sakai, Yusuke Makinae, Mana Kamigaito, Hidetaka Watanabe, Taro |
| author_facet | Sakai, Yusuke Makinae, Mana Kamigaito, Hidetaka Watanabe, Taro |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12299 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair Sakai, Yusuke Makinae, Mana Kamigaito, Hidetaka Watanabe, Taro Computation and Language Artificial Intelligence Machine Learning Sound Audio and Speech Processing 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}. |
| title | Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair |
| topic | Computation and Language Artificial Intelligence Machine Learning Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2404.12299 |