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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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Data for paper: "Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval" ...
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Data for paper: "Evaluating Resource-Lean Cross-Lingual Embedding Models in Unsupervised Retrieval" ...
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On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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Data for paper: "Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval" ...
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Evaluating multilingual text encoders for unsupervised cross-lingual retrieval
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Probing Pretrained Language Models for Lexical Semantics ...
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Probing Pretrained Language Models for Lexical Semantics ...
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Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers
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Glavas, Goran; Agic, Zeljko; Vulic, Ivan. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.345, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020
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Towards instance-level parser selection for cross-lingual transfer of dependency parsers
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How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions ...
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How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions
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Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
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Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only ...
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Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only
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