Search Results for author: Dominika Basaj

Found 9 papers, 3 papers with code

Visual Probing: Cognitive Framework for Explaining Self-Supervised Image Representations

1 code implementation21 Jun 2021 Witold Oleszkiewicz, Dominika Basaj, Igor Sieradzki, Michał Górszczak, Barbara Rychalska, Koryna Lewandowska, Tomasz Trzciński, Bartosz Zieliński

Motivated by this observation, we introduce a novel visual probing framework for explaining the self-supervised models by leveraging probing tasks employed previously in natural language processing.

Representation Learning

DiP Benchmark Tests: Evaluation Benchmarks for Discourse Phenomena in MT

no code implementations1 Jan 2021 Prathyusha Jwalapuram, Barbara Rychalska, Shafiq Joty, Dominika Basaj

Despite increasing instances of machine translation (MT) systems including extrasentential context information, the evidence for translation quality improvement is sparse, especially for discourse phenomena.

Machine Translation Translation

Can Your Context-Aware MT System Pass the DiP Benchmark Tests? : Evaluation Benchmarks for Discourse Phenomena in Machine Translation

no code implementations30 Apr 2020 Prathyusha Jwalapuram, Barbara Rychalska, Shafiq Joty, Dominika Basaj

Despite increasing instances of machine translation (MT) systems including contextual information, the evidence for translation quality improvement is sparse, especially for discourse phenomena.

Machine Translation Translation

How much should you ask? On the question structure in QA systems.

no code implementations WS 2018 Barbara Rychalska, Dominika Basaj, Anna Wr{\'o}blewska, Przemyslaw Biecek

Datasets that boosted state-of-the-art solutions for Question Answering (QA) systems prove that it is possible to ask questions in natural language manner.

Question Answering

How much should you ask? On the question structure in QA systems

no code implementations11 Sep 2018 Dominika Basaj, Barbara Rychalska, Przemyslaw Biecek, Anna Wroblewska

Datasets that boosted state-of-the-art solutions for Question Answering (QA) systems prove that it is possible to ask questions in natural language manner.

Question Answering

Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System

no code implementations WS 2018 Barbara Rychalska, Dominika Basaj, Przemyslaw Biecek, Anna Wroblewska

In this paper we present the results of an investigation of the importance of verbs in a deep learning QA system trained on SQuAD dataset.

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