@inproceedings{lee-etal-2017-l1,
    title = "{L}1-{L}2 Parallel Dependency Treebank as Learner Corpus",
    author = "Lee, John  and
      Li, Keying  and
      Leung, Herman",
    editor = "Miyao, Yusuke  and
      Sagae, Kenji",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/W17-6306/",
    pages = "44--49",
    abstract = "This opinion paper proposes the use of parallel treebank as learner corpus. We show how an L1-L2 parallel treebank {---} i.e., parse trees of non-native sentences, aligned to the parse trees of their target hypotheses {---} can facilitate retrieval of sentences with specific learner errors. We argue for its benefits, in terms of corpus re-use and interoperability, over a conventional learner corpus annotated with error tags. As a proof of concept, we conduct a case study on word-order errors made by learners of Chinese as a foreign language. We report precision and recall in retrieving a range of word-order error categories from L1-L2 tree pairs annotated in the Universal Dependency framework."
}