Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset   The MS MARCO ranking dataset has been widely used for training deep learning
models for IR tasks, achieving considerable effectiveness on diverse zero-shot
scenarios. However, this type of resource is scarce in languages other than
English. In this work, we present mMARCO, a multilingual version of the MS
MARCO passage ranking dataset comprising 13 languages that was created using
machine translation. We evaluated mMARCO by fine-tuning monolingual and
multilingual re-ranking models, as well as a dense multilingual model on this
dataset. Experimental results demonstrate that multilingual models fine-tuned
on our translated dataset achieve superior effectiveness to models fine-tuned
on the original English version alone. Our distilled multilingual re-ranker is
competitive with non-distilled models while having 5.4 times fewer parameters.
Lastly, we show a positive correlation between translation quality and
retrieval effectiveness, providing evidence that improvements in translation
methods might lead to improvements in multilingual information retrieval. The
translated datasets and fine-tuned models are available at
https://github.com/unicamp-dl/mMARCO.git.

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The categories of this paper are: q-bio
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The categories of this paper are: chem-ph
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The categories of this paper are: q-alg
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org