A Dense Representation Framework for Lexical and Semantic Matching
نویسندگان
چکیده
Lexical and semantic matching capture different successful approaches to text retrieval the fusion of their results has proven be more effective robust than either alone. Prior work performs hybrid by conducting lexical using systems (e.g., Lucene Faiss, respectively) then fusing model outputs. In contrast, our integrates representations with dense densifying high-dimensional into what we call low-dimensional (DLRs). Our experiments show that DLRs can effectively approximate original representations, preserving effectiveness while improving query latency. Furthermore, combine generate (DHRs) are flexible yield faster compared existing techniques. addition, explore jointly training in a single empirically resulting DHRs able advantages individual components. best DHR is competitive state-of-the-art single-vector multi-vector retrievers both in-domain zero-shot evaluation settings. requires smaller indexes, making representation framework an attractive approach retrieval. code available at https://github.com/castorini/dhr .
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ژورنال
عنوان ژورنال: ACM Transactions on Information Systems
سال: 2023
ISSN: ['1558-1152', '1558-2868', '1046-8188', '0734-2047']
DOI: https://doi.org/10.1145/3582426