PoolRank：列表学习和排名平衡的最大/最小基于池的排名损失
PoolRank: Max/Min Pooling-based Ranking Loss for Listwise Learning & Ranking Balance
摘要：
近年来，人们提出了许多神经检索模型。这些模型学习计算给定查询和文档之间的排名分数。大多数现有模型都是直接使用人类判断的标签以成对方式进行训练，而无需进一步校准。传统的成对方案可能很耗时，并且需要预定义的正负文档对进行训练，由于训练和测试条件之间的文档分布不匹配，可能导致学习偏差。一些流行的现有列表模式依赖于强预定义的概率假设和给定查询的相关和非相关文档之间的明显差异，这可能会由于低质量或模糊的相关性标签而限制模型潜力。为了解决这些问题，我们转向物理启发的排名平衡计划，并提出PoolRank，一个基于池的列表学习框架。该方案具有四个主要优点：（1）PoolRank基于模型性能和丰富文档候选者之间的相对排名，从局部最佳候选者中提取训练信息。(2)通过以多任务学习方式组合四个基于池的损失组件，PoolRank自动校准部分相关和高度不相关文档的排名平衡，而无需昂贵的人工检查。(3)PoolRank可以很容易地推广到任何神经检索模型，而不需要额外的可学习参数或模型结构修改。(4)与成对学习和现有的列表学习方案相比，PoolRank在保持有效收敛速度的同时，为所有研究的检索模型提供了更好的排名性能。
英文摘要：
Numerous neural retrieval models have been proposed in recent years. These models learn to compute a ranking score between the given query and document. The majority of existing models are trained in pairwise fashion using human-judged labels directly without further calibration. The traditional pairwise schemes can be time-consuming and require pre-defined positive-negative document pairs for training, potentially leading to learning bias due to document distribution mismatch between training and test conditions. Some popular existing listwise schemes rely on the strong pre-defined probabilistic assumptions and stark difference between relevant and non-relevant documents for the given query, which may limit the model potential due to the low-quality or ambiguous relevance labels. To address these concerns, we turn to a physics-inspired ranking balance scheme and propose PoolRank, a pooling-based listwise learning framework. The proposed scheme has four major advantages: (1) PoolRank extracts training information from the best candidates at the local level based on model performance and relative ranking among abundant document candidates. (2) By combining four pooling-based loss components in a multi-task learning fashion, PoolRank calibrates the ranking balance for the partially relevant and the highly non-relevant documents automatically without costly human inspection. (3) PoolRank can be easily generalized to any neural retrieval model without requiring additional learnable parameters or model structure modifications. (4) Compared to pairwise learning and existing listwise learning schemes, PoolRank yields better ranking performance for all studied retrieval models while retaining efficient convergence rates.
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。
