Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval

Xinyu Zhang, Xueguang Ma, Peng Shi, Jimmy Lin


Abstract
We present Mr. TyDi, a multi-lingual benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages, designed to evaluate ranking with learned dense representations. The goal of this resource is to spur research in dense retrieval techniques in non-English languages, motivated by recent observations that existing techniques for representation learning perform poorly when applied to out-of-distribution data. As a starting point, we provide zero-shot baselines for this new dataset based on a multi-lingual adaptation of DPR that we call “mDPR”. Experiments show that although the effectiveness of mDPR is much lower than BM25, dense representations nevertheless appear to provide valuable relevance signals, improving BM25 results in sparse–dense hybrids. In addition to analyses of our results, we also discuss future challenges and present a research agenda in multi-lingual dense retrieval. Mr. TyDi can be downloaded at https://github.com/castorini/mr.tydi.
Anthology ID:
2021.mrl-1.12
Volume:
Proceedings of the 1st Workshop on Multilingual Representation Learning
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | MRL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
127–137
Language:
URL:
https://aclanthology.org/2021.mrl-1.12
DOI:
10.18653/v1/2021.mrl-1.12
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.mrl-1.12.pdf
Code
 castorini/mr.tydi
Data
Mr. TYDIMS MARCONatural Questions