@inproceedings{wu-etal-2025-improve,
    title = "Improve Speech Translation Through Text Rewrite",
    author = "Wu, Jing  and
      Wang, Shushu  and
      Fan, Kai  and
      Luo, Wei  and
      Liao, Minpeng  and
      Huang, Zhongqiang",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven  and
      Darwish, Kareem  and
      Agarwal, Apoorv",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2025.coling-industry.28/",
    pages = "331--342",
    abstract = "Despite recent progress in Speech Translation (ST) research, the challenges posed by inherent speech phenomena that distinguish transcribed speech from written text are not well addressed. The informal and erroneous nature of spontaneous speech is inadequately represented in the typical parallel text available for building translation models. We propose to address these issues through a text rewrite approach that aims to transform transcribed speech into a cleaner style more in line with the expectations of translation models built from written text. Moreover, the advantages of the rewrite model can be effectively distilled into a standalone translation model. Experiments on several benchmarks, using both publicly available and in-house translation models, demonstrate that adding a rewrite model to a traditional ST pipeline is a cost-effect way to address a variety of speech irregularities and improve speech translation quality for multiple language directions and domains."
}