@inproceedings{flanagan-2014-bilingual,
    title = "Bilingual phrase-to-phrase alignment for arbitrarily-small datasets",
    author = "Flanagan, Kevin",
    editor = "Al-Onaizan, Yaser  and
      Simard, Michel",
    booktitle = "Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track",
    month = oct # " 22-26",
    year = "2014",
    address = "Vancouver, Canada",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2014.amta-researchers.7/",
    pages = "83--95",
    abstract = {This paper presents a novel system for sub-sentential alignment of bilingual sentence pairs, however few, using readily-available machine-readable bilingual dictionaries. Performance is evaluated against an existing gold-standard parallel corpus where word alignments are annotated, showing results that are a considerable improvement on a comparable system and on GIZA++ performance for the same corpus. Since na{\"i}ve application of the system for N languages would require N(N - 1) dictionaries, it is also evaluated using a pivot language, where only 2(N - 1) dictionaries would be required, with surprisingly similar performance. The system is proposed as an alternative to statistical methods, for use with very small corpora or for {\textquoteleft}on-the-fly' alignment.}
}