Papers by Piotr Rybak
NLPre: A Revised Approach towards Language-centric Benchmarking of Natural Language Preprocessing Systems (2024.lrec-main)
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| Challenge: | GLUE benchmarking system enables ongoing evaluation of multiple NLPre tools while credibly tracking their performance. |
| Approach: | They propose a language-centric benchmarking system that enables ongoing evaluation of multiple NLPre tools while credibly tracking their performance. |
| Outcome: | The proposed system is configured for Polish and integrated with the thoroughly assembled NLPre-PL benchmark. |
PolQA: Polish Question Answering Dataset (2024.lrec-main)
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| Challenge: | Recent proposed systems for open-domain question answering (OpenQA) require large amounts of training data to achieve state-of-the-art performance. |
| Approach: | They propose an efficient annotation strategy that increases passage retrieval accuracy@10 by 10.55 p.p. while reducing the annotation cost by 82%. |
| Outcome: | The proposed approach increases passage retrieval accuracy @10 by 10.55 p.p. while reducing the annotation cost by 82%. |
Going beyond research datasets: Novel intent discovery in the industry setting (2023.findings-eacl)
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| Challenge: | Novel intent discovery automates grouping of similar messages to identify previously unknown intents. |
| Approach: | They propose to use question-only data to improve the intent discovery pipeline . they propose to utilize conversational structure of real-life datasets for clustering . |
| Outcome: | The proposed method gives 33pp performance boost over state-of-the-art model for question only . it also gives 13pp performance increase over the naive baseline model . |
Transferring BERT Capabilities from High-Resource to Low-Resource Languages Using Vocabulary Matching (2024.lrec-main)
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| Challenge: | Pre-trained language models have revolutionized the natural language understanding landscape, but limited data hinders effective training of such models. |
| Approach: | They propose to transfer BERT capabilities from high-resource to low-resourced languages using vocabulary matching. |
| Outcome: | The proposed technique improves performance even when target language has minimal training data. |
Silver Retriever: Advancing Neural Passage Retrieval for Polish Question Answering (2024.lrec-main)
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| Challenge: | lexical approaches to find passages have outperformed lexicals due to their superior performance . however, for some languages, such as Polish, few models are available . a recent study shows that neural retrievers are more efficient and efficient than lexica. |
| Approach: | They present a neural retriever for Polish trained on a diverse collection of manual and weakly labeled datasets. |
| Outcome: | The proposed model outperforms lexical retrieval models in Polish on three retrieval tasks. |
KLEJ: Comprehensive Benchmark for Polish Language Understanding (2020.acl-main)
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| Challenge: | Recent introduction of robust, general-purpose models for fine-tuning has enabled improvements in general natural language understanding (NLU) but such benchmarks are only available for a handful of languages. |
| Approach: | They propose a multi-task benchmark for the Polish language understanding with an online leaderboard . they also propose GLUE, a task for named entity recognition and sentiment analysis . |
| Outcome: | The proposed model performs best on three out of nine tasks in the Polish language . the proposed model is also used in an e-commerce domain to analyze the sentiments of users . |
Evaluation of Transfer Learning for Polish with a Text-to-Text Model (2022.lrec-1)
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Aleksandra Chrabrowa, Łukasz Dragan, Karol Grzegorczyk, Dariusz Kajtoch, Mikołaj Koszowski, Robert Mroczkowski, Piotr Rybak
| Challenge: | Recent years have brought significant progress in natural language understanding (NLU) and natural language generation (NLG). |
| Approach: | They propose a benchmark for assessing the quality of text-to-text models for Polish . they evaluate the performance of plT5, mT5, Polish BART, and Polish GPT-2 . |
| Outcome: | The proposed model can be fine-tuned on various NLP tasks with a single training objective. |