On cross-lingual retrieval with multilingual text encoders.
Ponzetto, Simone Paolo
Inf Retr Boston
Springer Science and Business Media LLC
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Litschko, R., Vulić, I., Ponzetto, S. P., & Glavaš, G. (2022). On cross-lingual retrieval with multilingual text encoders.. Inf Retr Boston, 25 (2), 149-183. https://doi.org/10.1007/s10791-022-09406-x
Funder: Universität Mannheim (3157)
Pretrained multilingual text encoders based on neural transformer architectures, such as multilingual BERT (mBERT) and XLM, have recently become a default paradigm for cross-lingual transfer of natural language processing models, rendering cross-lingual word embedding spaces (CLWEs) effectively obsolete. 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 document-level CLIR, where we independently score a query against document sections. In the second part, we evaluate multilingual encoders fine-tuned in a supervised fashion (i.e., we learn to rank) on English relevance data in a series of zero-shot language and domain transfer CLIR experiments. Our results show that, despite the supervision, and due to the domain and language shift, supervised re-ranking rarely improves the performance of multilingual transformers as unsupervised base rankers. Finally, only with in-domain contrastive fine-tuning (i.e., same domain, only language transfer), we manage to improve the ranking quality. We uncover substantial empirical differences between cross-lingual retrieval results and results of (zero-shot) cross-lingual transfer for monolingual retrieval in target languages, which point to "monolingual overfitting" of retrieval models trained on monolingual (English) data, even if they are based on multilingual transformers.
Special Issue on ECIR 2021, Cross-lingual IR, Multilingual text encoders, Learning to Rank
European Research Council (957356)
Ministerium für Wirtschaft, Arbeit und Wohnungsbau Baden-Württemberg (MultiConvAI)
External DOI: https://doi.org/10.1007/s10791-022-09406-x
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336981