Cross-Lingual Training of Neural Models for Document Ranking
By Peng Shi, Richard He Bai, Jimmy J. Lin. 2020
23 citations recorded by the source corpus.
We tackle the challenge of cross-lingual training of neural document ranking models for mono-lingual retrieval, specifically leveraging relevance judgments in English to improve search in non-English languages. Our work successfully applies multi-lingual BERT (mBERT) to document ranking and additionally compares against a number of alternatives: translating the training data, translating documents, multi-stage hybrids, and ensembles. Experiments on test collections in six different languages from diverse language families reveal many interesting findings: model-based relevance transfer using mBERT can significantly improve search quality in (non-English) mono-lingual retrieval, but other “low resource” approaches are competitive as well.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Xueguang Ma
- Jimmy J. Lin
- Xinyu Crystina Zhang
- Peng Shi
- Qun Liu
- Nandan Thakur
- Mehdi Rezagholizadeh
- Ehsan Kamalloo
Citations in the corpus
- Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval (2021, background): “We have not yet systematically explored the broad design space of what Lin et al. (2020) calls “multi-step fine-tuning strategies”, par-alleling the explorations of Shi et al. (2020) in the context of transformer-based reranking models.”
- Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages (2022, background): “For these classes of retrieval models, there is an emerging thread of research [17, 18, 23, 29] we hope that MIRACL”