# Cross-lingual Document Retrieval using Regularized Wasserstein Distance

arXiv: https://arxiv.org/abs/1805.04437 · HTML: https://mlsanity.com/paper/1805.04437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2018.

## Built on by (most cited first, 1 of 1)

- [Supporting verification of news articles with automated search for semantically similar articles](https://mlsanity.com/paper/2103.15581/) (2021)

## Abstract (arXiv)

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.