Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval

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 inference, this modular design allows us to compose the ranker by applying the task adapter (or SFTM) trained with source language data together with the language adapter (or SFTM) of a target language. Besides improved transfer performance, these two approaches offer faster ranker training, with only a fraction of parameters being updated compared to full MMT fine-tuning. We benchmark our models on the CLEF-2003 benchmark, showing that our parameter-efficient methods outperform standard zero-shot transfer with full MMT fine-tuning, while enabling modularity and reducing training times. Further, we show on the example of Swahili and Somali that, for low(er)-resource languages, our parameter-efficient neural re-rankers can improve the ranking of the competitive machine translation-based ranker