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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.04620 |
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| _version_ | 1866909338221674496 |
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| author | Pokrywka, Jakub |
| author_facet | Pokrywka, Jakub |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04620 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Passage Retrieval of Polish Texts Using OKAPI BM25 and an Ensemble of Cross Encoders Pokrywka, Jakub Computation and Language Artificial Intelligence 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. |
| title | Passage Retrieval of Polish Texts Using OKAPI BM25 and an Ensemble of Cross Encoders |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2410.04620 |