# Passage Retrieval of Polish Texts Using OKAPI BM25 and an Ensemble of Cross Encoders

arXiv: https://arxiv.org/abs/2410.04620 · HTML: https://mlsanity.com/paper/2410.04620/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 6 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers](https://mlsanity.com/paper/2012.15828/) (2020)
- [mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset](https://mlsanity.com/paper/2108.13897/) (2021)
- [HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish](https://mlsanity.com/paper/2105.01735/) (2021)
- [Pre-training Polish Transformer-based Language Models at Scale](https://mlsanity.com/paper/2006.04229/) (2020)
- [BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language](https://mlsanity.com/paper/2305.19840/) (2023)
- [Evaluation of Transfer Learning for Polish with a Text-to-Text Model](https://mlsanity.com/paper/2205.08808/) (2022)

## Abstract (arXiv)

Passage Retrieval has traditionally relied on lexical methods like TF-IDF and BM25. Recently, some neural network models have surpassed these methods in performance. However, these models face challenges, such as the need for large annotated datasets and adapting to new domains. This paper presents a winning solution to the Poleval 2023 Task 3: Passage Retrieval challenge, which involves retrieving passages of Polish texts in three domains: trivia, legal, and customer support. However, only the trivia domain was used for training and development data. The method used the OKAPI BM25 algorithm to retrieve documents and an ensemble of publicly available multilingual Cross Encoders for Reranking. Fine-tuning the reranker models slightly improved performance but only in the training domain, while it worsened in other domains.