# Cross-Lingual Document Retrieval with Smooth Learning

arXiv: https://arxiv.org/abs/2011.00701 · HTML: https://mlsanity.com/paper/2011.00701/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Topological Information Retrieval with Dilation-Invariant Bottleneck Comparative Measures](https://mlsanity.com/paper/2104.01672/) (2021)
- [The Geometry of Multilingual Language Models: An Equality Lens](https://mlsanity.com/paper/2305.07839/) (2023)
- [LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models](https://mlsanity.com/paper/2501.00874/) (2025)

## Abstract (arXiv)

Cross-lingual document search is an information retrieval task in which the queries' language differs from the documents' language. In this paper, we study the instability of neural document search models and propose a novel end-to-end robust framework that achieves improved performance in cross-lingual search with different documents' languages. This framework includes a novel measure of the relevance, smooth cosine similarity, between queries and documents, and a novel loss function, Smooth Ordinal Search Loss, as the objective. We further provide theoretical guarantee on the generalization error bound for the proposed framework. We conduct experiments to compare our approach with other document search models, and observe significant gains under commonly used ranking metrics on the cross-lingual document retrieval task in a variety of languages.