@inproceedings{yang-etal-2019-maam,
    title = "{MAAM}: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction",
    author = "Yang, Pengcheng  and
      Luo, Fuli  and
      Chen, Peng  and
      Liu, Tianyu  and
      Sun, Xu",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/P19-1308/",
    doi = "10.18653/v1/P19-1308",
    pages = "3190--3196",
    abstract = "The task of unsupervised bilingual lexicon induction (UBLI) aims to induce word translations from monolingual corpora in two languages. Previous work has shown that morphological variation is an intractable challenge for the UBLI task, where the induced translation in failure case is usually morphologically related to the correct translation. To tackle this challenge, we propose a morphology-aware alignment model for the UBLI task. The proposed model aims to alleviate the adverse effect of morphological variation by introducing grammatical information learned by the pre-trained denoising language model. Results show that our approach can substantially outperform several state-of-the-art unsupervised systems, and even achieves competitive performance compared to supervised methods."
}