基于正则化Wasserstein距离的跨语言文档检索
Cross-lingual Document Retrieval using Regularized Wasserstein Distance
摘要：
许多信息检索算法依赖于允许有效地比较不同性质的对象的良好距离的概念。最近，一个新的有前途的度量被称为词移动者的距离被提出来衡量文本段落之间的分歧。在本文中，我们证明了这个度量可以扩展到包含术语加权方案，并使用熵正则化在文档之间提供更准确和计算效率更高的匹配。我们评估的好处，这两个扩展的跨语言文档检索（CLDR）的任务。我们对8个CLDR问题的实验结果表明，所提出的方法实现了显着的改善，平均倒数排名相比，几个基线。
英文摘要：
Many information retrieval algorithms rely on the notion of a good distance that allows to efficiently compare objects of different nature. Recently, a new promising metric called Word Mover's Distance was proposed to measure the divergence between text passages. In this paper, we demonstrate that this metric can be extended to incorporate term-weighting schemes and provide more accurate and computationally efficient matching between documents using entropic regularization. We evaluate the benefits of both extensions in the task of cross-lingual document retrieval (CLDR). Our experimental results on eight CLDR problems suggest that the proposed methods achieve remarkable improvements in terms of Mean Reciprocal Rank compared to several baselines.
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。
