Summary of Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair, By Yusuke Sakai et al.
Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair
by Yusuke Sakai, Mana Makinae, Hidetaka Kamigaito, Taro Watanabe
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a method to convert existing speech translation corpora into interpretation-style data, allowing for the training of high-quality yet low-latency Simultaneous Machine Translation (SiMT) systems. The approach uses Large Language Models (LLMs) to preserve the original word order and maintain the entire source content. By fine-tuning SiMT models in both text-to-text and speech-to-text settings with this converted corpus, the paper demonstrates a reduction in latency while maintaining the same level of quality as models trained on offline datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps improve Simultaneous Machine Translation systems by creating a new way to use existing language translation data. The researchers found a way to change how we prepare speech translation data so that it’s better for training machine translation models. This makes the translation process faster and just as good, which is useful in many situations. |
Keywords
» Artificial intelligence » Fine tuning » Translation
