Exploring Snippets as a Dataset to Overcome Challenges in CLIR

Abstract

Cross-lingual information retrieval (CLIR) is a challenging task that requires overcoming linguistic barriers to match user queries with relevant documents in different languages. One of the major challenges in CLIR is the lack of parallel corpora, which hinders the development of effective translation models. This challenge can be addressed using snippets as a dataset to train CLIR models. Snippets can be automatically extracted from various sources, such as search engine result pages and can provide a rich and diverse set of collections for cross-lingual information retrieval. This paper initially discusses the challenges in CLIR and then explores the use of snippets as a dataset which can lead towards the development or improvements in the techniques to improve the retrieval effectiveness and further discusses the advantages and limitations of using snippets dataset in CLIR.