@inproceedings{carpuat-etal-2017-detecting,
    title = "Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation",
    author = "Carpuat, Marine  and
      Vyas, Yogarshi  and
      Niu, Xing",
    editor = "Luong, Thang  and
      Birch, Alexandra  and
      Neubig, Graham  and
      Finch, Andrew",
    booktitle = "Proceedings of the First Workshop on Neural Machine Translation",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/W17-3209/",
    doi = "10.18653/v1/W17-3209",
    pages = "69--79",
    abstract = "Parallel corpora are often not as parallel as one might assume: non-literal translations and noisy translations abound, even in curated corpora routinely used for training and evaluation. We use a cross-lingual textual entailment system to distinguish sentence pairs that are parallel in meaning from those that are not, and show that filtering out divergent examples from training improves translation quality."
}