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Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
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Abstract:
State-of-the-art neural (re)rankers are notoriously data hungry which - given the lack of large-scale training data in languages other than English - makes them rarely used in multilingual and cross-lingual retrieval settings. Current approaches therefore typically transfer rankers trained on English data to other languages and cross-lingual setups by means of multilingual encoders: they fine-tune all the parameters of a pretrained massively multilingual Transformer (MMT, e.g., multilingual BERT) on English relevance judgments and then deploy it in the target language. In this work, we show that two parameter-efficient approaches to cross-lingual transfer, namely Sparse Fine-Tuning Masks (SFTMs) and Adapters, allow for a more lightweight and more effective zero-shot transfer to multilingual and cross-lingual retrieval tasks. We first train language adapters (or SFTMs) via Masked Language Modelling and then train retrieval (i.e., reranking) adapters (SFTMs) on top while keeping all other parameters fixed. At ...
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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.2204.02292
https://arxiv.org/abs/2204.02292
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