Why is a document relevant? Understanding the relevance scores in cross-lingual document retrieval

作者：

Highlights：

• Theoretical background on using optimal transport for measuring document relevancy.

• A novel learning-to-rank model outputting interpretable document relevance scores.

• The model performs best on high-resource language pair data sets.

• Performs an analysis of the impact the loss function has on the model’s performance.

摘要

•Theoretical background on using optimal transport for measuring document relevancy.•A novel learning-to-rank model outputting interpretable document relevance scores.•The model performs best on high-resource language pair data sets.•Performs an analysis of the impact the loss function has on the model’s performance.

论文关键词：00-01,99-00,Cross-lingual information retrieval,Language model,Optimal transport,Result interpretability,Natural language processing

论文评审过程：Received 31 October 2021, Revised 11 February 2022, Accepted 3 March 2022, Available online 10 March 2022, Version of Record 21 March 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.108545