Domain Adaptation of Multilingual Semantic Search -- Literature Review

TL;DR

This paper reviews current methods for domain adaptation and multilingual semantic search in low-resource settings, introduces a new typology for clustering adaptation approaches, and explores their combination for dense retrieval systems.

Contribution

It presents a novel typology for classifying domain adaptation methods and investigates their integration with multilingual semantic search in low-resource environments.

Findings

New typology for domain adaptation approaches

Potential for combining multilingual search with domain adaptation

Framework for efficient dense retrieval in low-resource settings

Abstract

This literature review gives an overview of current approaches to perform domain adaptation in a low-resource and approaches to perform multilingual semantic search in a low-resource setting. We developed a new typology to cluster domain adaptation approaches based on the part of dense textual information retrieval systems, which they adapt, focusing on how to combine them efficiently. We also explore the possibilities of combining multilingual semantic search with domain adaptation approaches for dense retrievers in a low-resource setting.