21 citations · 42 across the 8 of their papers we have counts for
10 papers
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…
MAUPQA: Massive Automatically-created Polish Question Answering Dataset
Piotr Rybak
Recently, open-domain question answering systems have begun to rely heavily on annotated datasets to train neural passage retrievers. However, manually annotating such datasets is…
Going beyond research datasets: Novel intent discovery in the industry setting
Aleksandra Chrabrowa, Tsimur Hadeliya, Dariusz Kajtoch +2
Novel intent discovery automates the process of grouping similar messages (questions) to identify previously unknown intents. However, current research focuses on publicly availabl…
PolQA: Polish Question Answering Dataset
Piotr Rybak, Piotr Przybyła, Maciej Ogrodniczuk
Recently proposed systems for open-domain question answering (OpenQA) require large amounts of training data to achieve state-of-the-art performance. However, data annotation is kn…
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…
Semi-Supervised Neural System for Tagging, Parsing and Lematization
Piotr Rybak, Alina Wróblewska
This paper describes the ICS PAS system which took part in CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. The system consists of jointly tr…