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- W4381551790 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 https://github. com/panuthept/DST-DenseRetrieval." @default.
- W4381551790 created "2023-06-22" @default.
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- W4381551790 date "2023-06-17" @default.
- W4381551790 modified "2023-09-26" @default.
- W4381551790 title "Typo-Robust Representation Learning for Dense Retrieval" @default.
- W4381551790 doi "https://doi.org/10.48550/arxiv.2306.10348" @default.
- W4381551790 hasPublicationYear "2023" @default.
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