@inproceedings{tasawong-etal-2023-typo,
    title = "Typo-Robust Representation Learning for Dense Retrieval",
    author = "Tasawong, Panuthep  and
      Ponwitayarat, Wuttikorn  and
      Limkonchotiwat, Peerat  and
      Udomcharoenchaikit, Can  and
      Chuangsuwanich, Ekapol  and
      Nutanong, Sarana",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.95",
    doi = "10.18653/v1/2023.acl-short.95",
    pages = "1106--1115",
    abstract = "Dense retrieval is a basic building block of information retrieval applications. One of the main challenges of dense retrieval in real-world settings is the handling of queries containing misspelled words. A popular approach for handling misspelled queries is minimizing the representations discrepancy between misspelled queries and their pristine ones. Unlike the existing approaches, which only focus on the alignment between misspelled and pristine queries, our method also improves the contrast between each misspelled query and its surrounding queries. To assess the effectiveness of our proposed method, we compare it against the existing competitors using two benchmark datasets and two base encoders. Our method outperforms the competitors in all cases with misspelled queries. Our code and models are available at \url{https://github.com/panuthept/DST-DenseRetrieval}.",
}

Dense retrieval is a basic building block of information retrieval applications. One of the main challenges of dense retrieval in real-world settings is the handling of queries containing misspelled words. A popular approach for handling misspelled queries is minimizing the representations discrepancy between misspelled queries and their pristine ones. Unlike the existing approaches, which only focus on the alignment between misspelled and pristine queries, our method also improves the contrast between each misspelled query and its surrounding queries. To assess the effectiveness of our proposed method, we compare it against the existing competitors using two benchmark datasets and two base encoders. Our method outperforms the competitors in all cases with misspelled queries. Our code and models are available at \url{https://github.com/panuthept/DST-DenseRetrieval}.
Tasawong, Panuthep Ponwitayarat, Wuttikorn Limkonchotiwat, Peerat Udomcharoenchaikit, Can Chuangsuwanich, Ekapol Nutanong, Sarana
Typo-Robust Representation Learning for Dense Retrieval