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Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
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On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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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 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; H.3.3; I.2.7; Information Retrieval cs.IR
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URL: https://dx.doi.org/10.48550/arxiv.2112.11031
https://arxiv.org/abs/2112.11031
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Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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