A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Learning to Enrich Query Representation with Pseudo-Relevance Feedback for Cross-lingual Retrieval
2022
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Cross-lingual information retrieval (CLIR) aims to provide access to information across languages. Recent pre-trained multilingual language models brought large improvements to the natural language tasks, including cross-lingual adhoc retrieval. However, pseudorelevance feedback (PRF), a family of techniques for improving ranking using the contents of top initially retrieved items, has not been explored with neural CLIR retrieval models. Two of the challenges are incorporating feedback from
doi:10.1145/3477495.3532013
fatcat:uee56yexcrc75cmdypw3liffom