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Hits 1 – 3 of 3
1
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
Litschko, Robert
;
Vulić, Ivan
;
Glavaš, Goran
. - : arXiv, 2022
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2
On Cross-Lingual Retrieval with Multilingual Text Encoders ...
Litschko, Robert
;
Vulić, Ivan
;
Ponzetto, Simone Paolo
;
Glavaš, Goran
. - : arXiv, 2021
Abstract:
In this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks across a number of diverse language pairs. We first treat these models as multilingual text encoders and benchmark their performance in unsupervised ad-hoc sentence- and document-level CLIR. In contrast to supervised language understanding, our results indicate that for unsupervised document-level CLIR -- a setup with no relevance judgments for IR-specific fine-tuning -- pretrained multilingual encoders on average fail to significantly outperform earlier models based on CLWEs. For sentence-level retrieval, we do obtain state-of-the-art performance: the peak scores, however, are met by multilingual encoders that have been further specialized, in a supervised fashion, for sentence understanding tasks, rather than using their vanilla 'off-the-shelf' variants. Following these results, we introduce localized relevance matching for ... : to appear in IRJ ECIR 2021 Special Issue. arXiv admin note: substantial text overlap with arXiv:2101.08370 ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
;
H.3.3; I.2.7
;
Information Retrieval cs.IR
URL:
https://dx.doi.org/10.48550/arxiv.2112.11031
https://arxiv.org/abs/2112.11031
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3
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
Litschko, Robert
;
Vulić, Ivan
;
Ponzetto, Simone Paolo
. - : arXiv, 2021
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