DUQGen：通过多样化合成查询生成实现神经排序器的有效无监督域适应
DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation
AI总结：
针对神经排序器目标域标注数据稀缺问题，提出DUQGen，通过文档聚类与概率采样生成有效且多样的合成查询，实现无监督域适应，并在BEIR上显著提升排序性能。
摘要：
在MS-MARCO等大规模任务特定训练数据上预训练的最先进神经排序器，已被证明无需域适应即可在各种排序任务上表现出强大性能，这也称为零样本。然而，零样本神经排序可能并非最优，因为它没有利用目标域信息。遗憾的是，获取足够大规模且高质量的目标训练数据来改进现代神经排序器，可能成本高昂且耗时。为解决这一问题，我们提出了一种用于排序的无监督域适应新方法DUQGen，它弥补了以往文献中的一个关键空白，即如何自动生成既有效又多样的合成训练数据，以针对新域微调现代神经排序器。具体而言，DUQGen通过识别相似文档簇，生成更有效的目标域表示；并通过对所得文档簇进行概率采样，生成更多样化的训练数据集。我们在标准BEIR集合上进行的大量实验表明，DUQGen始终优于所有零样本基线，并在18个数据集中的16个上大幅优于SOTA基线，在所有数据集上平均实现4%的相对提升。我们还以详尽分析补充这些结果，以更深入地理解所提方法的性能，并确定有前景的进一步改进方向。
英文摘要：
State-of-the-art neural rankers pre-trained on large task-specific training data such as MS-MARCO, have been shown to exhibit strong performance on various ranking tasks without domain adaptation, also called zero-shot. However, zero-shot neural ranking may be sub-optimal, as it does not take advantage of the target domain information. Unfortunately, acquiring sufficiently large and high quality target training data to improve a modern neural ranker can be costly and time-consuming. To address this problem, we propose a new approach to unsupervised domain adaptation for ranking, DUQGen, which addresses a critical gap in prior literature, namely how to automatically generate both effective and diverse synthetic training data to fine tune a modern neural ranker for a new domain. Specifically, DUQGen produces a more effective representation of the target domain by identifying clusters of similar documents; and generates a more diverse training dataset by probabilistic sampling over the resulting document clusters. Our extensive experiments, over the standard BEIR collection, demonstrate that DUQGen consistently outperforms all zero-shot baselines and substantially outperforms the SOTA baselines on 16 out of 18 datasets, for an average of 4% relative improvement across all datasets. We complement our results with a thorough analysis for more in-depth understanding of the proposed method's performance and to identify promising areas for further improvements.
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。
