@inproceedings{behzad-etal-2023-effect,
    title = "The Effect of Alignment Correction on Cross-Lingual Annotation Projection",
    author = "Behzad, Shabnam  and
      Ebner, Seth  and
      Marone, Marc  and
      Van Durme, Benjamin  and
      Yarmohammadi, Mahsa",
    editor = "Prange, Jakob  and
      Friedrich, Annemarie",
    booktitle = "Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.law-1.24",
    doi = "10.18653/v1/2023.law-1.24",
    pages = "244--251",
    abstract = "Cross-lingual annotation projection is a practical method for improving performance on low resource structured prediction tasks. An important step in annotation projection is obtaining alignments between the source and target texts, which enables the mapping of annotations across the texts. By manually correcting automatically generated alignments, we examine the impact of alignment quality{---}automatic, manual, and mixed{---}on downstream performance for two information extraction tasks and quantify the trade-off between annotation effort and model performance.",
}

Cross-lingual annotation projection is a practical method for improving performance on low resource structured prediction tasks. An important step in annotation projection is obtaining alignments between the source and target texts, which enables the mapping of annotations across the texts. By manually correcting automatically generated alignments, we examine the impact of alignment quality{---}automatic, manual, and mixed{---}on downstream performance for two information extraction tasks and quantify the trade-off between annotation effort and model performance.
Behzad, Shabnam Ebner, Seth Marone, Marc Van Durme, Benjamin Yarmohammadi, Mahsa
The Effect of Alignment Correction on Cross-Lingual Annotation Projection