@inproceedings{kunchukuttan-etal-2017-utilizing,
    title = "Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based {SMT}",
    author = "Kunchukuttan, Anoop  and
      Shah, Maulik  and
      Prakash, Pradyot  and
      Bhattacharyya, Pushpak",
    editor = "Kondrak, Greg  and
      Watanabe, Taro",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://preview.aclanthology.org/landing_page/I17-2048/",
    pages = "283--289",
    abstract = "We investigate pivot-based translation between related languages in a low resource, phrase-based SMT setting. We show that a subword-level pivot-based SMT model using a related pivot language is substantially better than word and morpheme-level pivot models. It is also highly competitive with the best direct translation model, which is encouraging as no direct source-target training corpus is used. We also show that combining multiple related language pivot models can rival a direct translation model. Thus, the use of subwords as translation units coupled with multiple related pivot languages can compensate for the lack of a direct parallel corpus."
}