BEIR-NL: Zero-shot Information Retrieval Benchmark for the Dutch Language
This addresses the problem of underrepresented languages in IR for Dutch researchers and practitioners, but it is incremental as it adapts an existing benchmark through translation.
The authors tackled the lack of a zero-shot information retrieval benchmark for Dutch by introducing BEIR-NL, created by automatically translating the English BEIR datasets, and found that BM25 remains competitive, only outperformed by larger dense models, with translation causing performance drops in back-translation experiments.
Zero-shot evaluation of information retrieval (IR) models is often performed using BEIR; a large and heterogeneous benchmark composed of multiple datasets, covering different retrieval tasks across various domains. Although BEIR has become a standard benchmark for the zero-shot setup, its exclusively English content reduces its utility for underrepresented languages in IR, including Dutch. To address this limitation and encourage the development of Dutch IR models, we introduce BEIR-NL by automatically translating the publicly accessible BEIR datasets into Dutch. Using BEIR-NL, we evaluated a wide range of multilingual dense ranking and reranking models, as well as the lexical BM25 method. Our experiments show that BM25 remains a competitive baseline, and is only outperformed by the larger dense models trained for retrieval. When combined with reranking models, BM25 achieves performance on par with the best dense ranking models. In addition, we explored the impact of translation on the data by back-translating a selection of datasets to English, and observed a performance drop for both dense and lexical methods, indicating the limitations of translation for creating benchmarks. BEIR-NL is publicly available on the Hugging Face hub.