Cross-language Information Retrieval

2021/11/10 by Petra Galuščáková, Douglas W. Oard, Galuščáková, Petra +3
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Information Retrieval and Search Behavior #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2111.05988

openalex publication_date 2021/11/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Two key assumptions shape the usual view of ranked retrieval: (1) that the searcher can choose words for their query that might appear in the documents that they wish to see, and (2) that ranking retrieved documents will suffice because the searcher will be able to recognize those which they wished to find. When the documents to be searched are in a language not known by the searcher, neither assumption is true. In such cases, Cross-Language Information Retrieval (CLIR) is needed. This chapter reviews the state of the art for CLIR and outlines some open research questions.

Citations

• Efficient Estimation of Word Representations in Vector Space
• Generative Adversarial Networks
• A Generalized Solution of the Orthogonal Procrustes Problem
• Exploiting Similarities among Languages for Machine Translation
• Passage Re-ranking with BERT
• XLNet: Generalized Autoregressive Pretraining for Language Understanding
• Exploiting Neural Query Translation into Cross Lingual Information Retrieval

Related

• Bridging Language Gaps: Advances in Cross-Lingual Information Retrieval with Multilingual LLMs
• What Drives Cross-lingual Ranking? Retrieval Approaches with Multilingual Language Models
• Beyond Ranked Lists: The SARAL Framework for Cross-Lingual Document Set Retrieval
• Applying Machine Translation to Two-Stage Cross-Language Information Retrieval
• Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval