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- W4225500303 abstract "Query-by-document (QBD) retrieval is an Information Retrieval task in which a seed document acts as the query and the goal is to retrieve related documents – it is particular common in professional search tasks. In this work we improve the retrieval effectiveness of the BERT re-ranker, proposing an extension to its fine-tuning step to better exploit the context of queries. To this end, we use an additional document-level representation learning objective besides the ranking objective when fine-tuning the BERT re-ranker. Our experiments on two QBD retrieval benchmarks show that the proposed multi-task optimization significantly improves the ranking effectiveness without changing the BERT re-ranker or using additional training samples. In future work, the generalizability of our approach to other retrieval tasks should be further investigated." @default.
- W4225500303 created "2022-05-05" @default.
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- W4225500303 date "2022-01-01" @default.
- W4225500303 modified "2023-10-13" @default.
- W4225500303 title "Improving BERT-based Query-by-Document Retrieval with Multi-task Optimization" @default.
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- W4225500303 doi "https://doi.org/10.1007/978-3-030-99739-7_1" @default.
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