Search Results for author: Filip Graliński

Found 11 papers, 5 papers with code

Challenging America: Modeling language in longer time scales

no code implementations Findings (NAACL) 2022 Jakub Pokrywka, Filip Graliński, Krzysztof Jassem, Karol Kaczmarek, Krzysztof Jurkiewicz, Piotr Wierzchon

The aim of the paper is to apply, for historical texts, the methodology used commonly to solve various NLP tasks defined for contemporary data, i. e. pre-train and fine-tune large Transformer models.

Cloze Test Optical Character Recognition (OCR)

From Dataset Recycling to Multi-Property Extraction and Beyond

1 code implementation CONLL 2020 Tomasz Dwojak, Michał Pietruszka, Łukasz Borchmann, Jakub Chłędowski, Filip Graliński

This paper investigates various Transformer architectures on the WikiReading Information Extraction and Machine Reading Comprehension dataset.

Machine Reading Comprehension

Successive Halving Top-k Operator

1 code implementation8 Oct 2020 Michał Pietruszka, Łukasz Borchmann, Filip Graliński

We propose a differentiable successive halving method of relaxing the top-k operator, rendering gradient-based optimization possible.

On the Multi-Property Extraction and Beyond

no code implementations15 Jun 2020 Tomasz Dwojak, Michał Pietruszka, Łukasz Borchmann, Filip Graliński, Jakub Chłędowski

In this paper, we investigate the Dual-source Transformer architecture on the WikiReading information extraction and machine reading comprehension dataset.

Machine Reading Comprehension

ApplicaAI at SemEval-2020 Task 11: On RoBERTa-CRF, Span CLS and Whether Self-Training Helps Them

no code implementations SEMEVAL 2020 Dawid Jurkiewicz, Łukasz Borchmann, Izabela Kosmala, Filip Graliński

This paper presents the winning system for the propaganda Technique Classification (TC) task and the second-placed system for the propaganda Span Identification (SI) task.

Propaganda span identification

Kleister: A novel task for Information Extraction involving Long Documents with Complex Layout

no code implementations4 Mar 2020 Filip Graliński, Tomasz Stanisławek, Anna Wróblewska, Dawid Lipiński, Agnieszka Kaliska, Paulina Rosalska, Bartosz Topolski, Przemysław Biecek

State-of-the-art solutions for Natural Language Processing (NLP) are able to capture a broad range of contexts, like the sentence-level context or document-level context for short documents.

named-entity-recognition Named Entity Recognition +1

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