NeuralMind-UNICAMP at 2022 TREC NeuCLIR: Large Boring Rerankers for Cross-lingual Retrieval

March 28, 2023 · Entered Twilight · 🏛 Text Retrieval Conference

Repo contents: .gitignore, README.md, make_corpus&queries.ipynb, qrels_modified.fa, qrels_modified.ru, qrels_modified.zh, requirement.txt, runs, topics.0720.utf8.jsonl

Authors Vitor Jeronymo, Roberto Lotufo, Rodrigo Nogueira arXiv ID 2303.16145 Category cs.IR: Information Retrieval Citations 12 Venue Text Retrieval Conference Repository https://github.com/unicamp-dl/NeuCLIR22-mT5 ⭐ 2 Last Checked 6 months ago

Abstract

This paper reports on a study of cross-lingual information retrieval (CLIR) using the mT5-XXL reranker on the NeuCLIR track of TREC 2022. Perhaps the biggest contribution of this study is the finding that despite the mT5 model being fine-tuned only on query-document pairs of the same language it proved to be viable for CLIR tasks, where query-document pairs are in different languages, even in the presence of suboptimal first-stage retrieval performance. The results of the study show outstanding performance across all tasks and languages, leading to a high number of winning positions. Finally, this study provides valuable insights into the use of mT5 in CLIR tasks and highlights its potential as a viable solution. For reproduction refer to https://github.com/unicamp-dl/NeuCLIR22-mT5