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arxiv 2303.16145 v1 pith:CIUDRT5Y submitted 2023-03-28 cs.IR

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

Vitor Jeronymo , Roberto Lotufo , Rodrigo Nogueira This is my paper
classification cs.IR
keywords clirretrievaltaskscross-linguallanguagesneuclirpairsperformance
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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

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