浅层与深层相关性判断对基于BERT的重排序模型的影响
Impact of Shallow vs. Deep Relevance Judgments on BERT-based Reranking Models
AI总结：
本文研究了浅层与深层相关性判断对基于BERT的重排序模型性能的影响，发现浅层判断数据集因上下文更广能提升模型泛化能力，而深层判断数据集的劣势可通过增加负训练样本缓解。
摘要：
本文研究了浅层与深层相关性判断对神经信息检索中基于BERT的重排序模型性能的影响。研究比较了浅层判断数据集（特征为大量查询，每个查询仅有少量相关性判断）和深层判断数据集（涉及较少查询，但具有大量相关性判断）。研究评估了这些数据集如何影响在其上训练的基于BERT的重排序模型的性能。实验在MS MARCO和LongEval数据集上进行。结果表明，由于可用上下文范围更广，浅层判断数据集通常能提升重排序模型的泛化能力和有效性。深层判断数据集的劣势可能通过大量负训练样本来缓解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
