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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.23191 |
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| _version_ | 1866913917438001152 |
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| author | Iturra-Bocaz, Gabriel Vo, Danny Galuscakova, Petra |
| author_facet | Iturra-Bocaz, Gabriel Vo, Danny Galuscakova, Petra |
| contents | This paper investigates the impact of shallow versus deep relevance judgments on the performance of BERT-based reranking models in neural Information Retrieval. Shallow-judged datasets, characterized by numerous queries each with few relevance judgments, and deep-judged datasets, involving fewer queries with extensive relevance judgments, are compared. The research assesses how these datasets affect the performance of BERT-based reranking models trained on them. The experiments are run on the MS MARCO and LongEval collections. Results indicate that shallow-judged datasets generally enhance generalization and effectiveness of reranking models due to a broader range of available contexts. The disadvantage of the deep-judged datasets might be mitigated by a larger number of negative training examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23191 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Impact of Shallow vs. Deep Relevance Judgments on BERT-based Reranking Models Iturra-Bocaz, Gabriel Vo, Danny Galuscakova, Petra Information Retrieval This paper investigates the impact of shallow versus deep relevance judgments on the performance of BERT-based reranking models in neural Information Retrieval. Shallow-judged datasets, characterized by numerous queries each with few relevance judgments, and deep-judged datasets, involving fewer queries with extensive relevance judgments, are compared. The research assesses how these datasets affect the performance of BERT-based reranking models trained on them. The experiments are run on the MS MARCO and LongEval collections. Results indicate that shallow-judged datasets generally enhance generalization and effectiveness of reranking models due to a broader range of available contexts. The disadvantage of the deep-judged datasets might be mitigated by a larger number of negative training examples. |
| title | Impact of Shallow vs. Deep Relevance Judgments on BERT-based Reranking Models |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2506.23191 |