@inproceedings{jones-wijaya-2021-majority,
    title = "Majority Voting with Bidirectional Pre-translation For Bitext Retrieval",
    author = "Jones, Alexander  and
      Wijaya, Derry Tanti",
    editor = "Rapp, Reinhard  and
      Sharoff, Serge  and
      Zweigenbaum, Pierre",
    booktitle = "Proceedings of the 14th Workshop on Building and Using Comparable Corpora (BUCC 2021)",
    month = sep,
    year = "2021",
    address = "Online (Virtual Mode)",
    publisher = "INCOMA Ltd.",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.bucc-1.7/",
    pages = "46--59",
    abstract = "Obtaining high-quality parallel corpora is of paramount importance for training NMT systems. However, as many language pairs lack adequate gold-standard training data, a popular approach has been to mine so-called {\textquotedblleft}pseudo-parallel{\textquotedblright} sentences from paired documents in two languages. In this paper, we outline some drawbacks with current methods that rely on an embedding similarity threshold, and propose a heuristic method in its place. Our method involves translating both halves of a paired corpus before mining, and then performing a majority vote on sentence pairs mined in three ways: after translating documents in language x to language y, after translating language y to x, and using the original documents in languages x and y. We demonstrate success with this novel approach on the Tatoeba similarity search benchmark in 64 low-resource languages, and on NMT in Kazakh and Gujarati. We also uncover the effect of resource-related factors (i.e. how much monolingual/bilingual data is available for a given language) on the optimal choice of bitext mining method, demonstrating that there is currently no one-size-fits-all approach for this task. We make the code and data used in our experiments publicly available."
}