mlsanityData to 14 Oct 2026

Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair

arXiv 2404.12299 ↗First posted 18 Apr 2024cs.CL cs.AI cs.LGcited by 3

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Abstract (arXiv)

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 urlhttps://github.com/yusuke1997/LLM-SI-Corpus.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 43 of its 81 references that are in the map. First posted on arXiv 18 Apr 2024.

Builds on

43 of 81 references.

References · 43

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31 more
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Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2025Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Abilitycited by 10
  2. 2025LLMs Can Achieve High-quality Simultaneous Machine Translation as Efficiently as Offlinecited by 5
  3. 2024Word Order in English-Japanese Simultaneous Interpretation: Analyses and Evaluation using Chunk-wise Monotonic Translationcited by 1