@inproceedings{peitz-etal-2014-better,
    title = "Better punctuation prediction with hierarchical phrase-based translation",
    author = "Peitz, Stephan  and
      Freitag, Markus  and
      Ney, Hermann",
    editor = {Federico, Marcello  and
      St{\"u}ker, Sebastian  and
      Yvon, Fran{\c{c}}ois},
    booktitle = "Proceedings of the 11th International Workshop on Spoken Language Translation: Papers",
    month = dec # " 4-5",
    year = "2014",
    address = "Lake Tahoe, California",
    url = "https://preview.aclanthology.org/fix-sig-urls/2014.iwslt-papers.17/",
    pages = "271--278",
    abstract = "Punctuation prediction is an important task in spoken language translation and can be performed by using a monolingual phrase-based translation system to translate from unpunctuated to text with punctuation. However, a punctuation prediction system based on phrase-based translation is not able to capture long-range dependencies between words and punctuation marks. In this paper, we propose to employ hierarchical translation in place of phrase-based translation and show that this approach is more robust for unseen word sequences. Furthermore, we analyze different optimization criteria for tuning the scaling factors of a monolingual statistical machine translation system. In our experiments, we compare the new approach with other punctuation prediction methods and show improvements in terms of F1-Score and BLEU on the IWSLT 2014 German{\textrightarrow}English and English{\textrightarrow}French translation tasks."
}