21 citations · 23 across the 3 of their papers we have counts for
7 papers · 1 filter
NLPre: a revised approach towards language-centric benchmarking of Natural Language Preprocessing systems
Martyna Wiącek, Piotr Rybak, Łukasz Pszenny +1
With the advancements of transformer-based architectures, we observe the rise of natural language preprocessing (NLPre) tools capable of solving preliminary NLP tasks (e.g. tokenis…
Transferring BERT Capabilities from High-Resource to Low-Resource Languages Using Vocabulary Matching
Piotr Rybak
Pre-trained language models have revolutionized the natural language understanding landscape, most notably BERT (Bidirectional Encoder Representations from Transformers). However,…
Silver Retriever: Advancing Neural Passage Retrieval for Polish Question Answering
Piotr Rybak, Maciej Ogrodniczuk
Modern open-domain question answering systems often rely on accurate and efficient retrieval components to find passages containing the facts necessary to answer the question. Rece…
Evaluation of Transfer Learning for Polish with a Text-to-Text Model
Aleksandra Chrabrowa, Łukasz Dragan, Karol Grzegorczyk +4
We introduce a new benchmark for assessing the quality of text-to-text models for Polish. The benchmark consists of diverse tasks and datasets: KLEJ benchmark adapted for text-to-t…
HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish
Robert Mroczkowski, Piotr Rybak, Alina Wróblewska +1
BERT-based models are currently used for solving nearly all Natural Language Processing (NLP) tasks and most often achieve state-of-the-art results. Therefore, the NLP community co…
KLEJ: Comprehensive Benchmark for Polish Language Understanding
Piotr Rybak, Robert Mroczkowski, Janusz Tracz +1
In recent years, a series of Transformer-based models unlocked major improvements in general natural language understanding (NLU) tasks. Such a fast pace of research would not be p…