Papers by Michał Perełkiewicz
PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods (2024.lrec-main)
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| Challenge: | PIRB is a framework for text information retrieval in Polish . existing and new datasets are evaluated to evaluate the performance of 41 models . |
| Approach: | They propose a framework for 41 text information retrieval tasks in Polish . they evaluate over 20 dense and sparse retrieval models and build sparser-dense hybrid retrievers . |
| Outcome: | The proposed framework outperforms the best available methods in 41 tasks for Polish . the proposed models outperformed the best solutions available to date . |
Evaluation of Sentence Representations in Polish (2020.lrec-1)
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| Challenge: | Existing methods for learning sentence representations have been limited in low-resource languages such as Polish . |
| Approach: | They propose two new Polish datasets for evaluating sentence embeddings and evaluate eight different methods including Polish and multilingual models. |
| Outcome: | The proposed methods show strengths and weaknesses in Polish and multilingual models. |
Unveiling Dual Quality in Product Reviews: An NLP-Based Approach (2025.acl-industry)
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| Challenge: | Dual quality is a problem where products with identical ingredients or characteristics are sold under the same brand and similar packaging in different markets, but are significantly altered in composition or quality parameters. |
| Approach: | They propose to use natural language processing to detect inconsistent product quality by analyzing a Polish-language dataset and using different approaches. |
| Outcome: | The proposed approach can detect and address inconsistent product quality in Polish and other languages. |
Annobot: Platform for Annotating and Creating Datasets through Conversation with a Chatbot (2020.coling-demos)
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| Challenge: | Using conversation with a chatbot, we create annotating and creating datasets through conversation with an open-source platform called Annobot. |
| Approach: | They propose an open-source platform for annotating and creating datasets through conversation with a chatbot. |
| Outcome: | The proposed platform has a wide range of applications including data labelling for binary, multi-class/label classification tasks, preparing data for regression problems and creating sets for issues such as machine translation, question answering or text summarization. |