Zuzanna Bordzicka


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Samsung Research Poland (SRPOL) at SemEval-2022 Task 9: Hybrid Question Answering Using Semantic Roles
Tomasz Dryjański | Monika Zaleska | Bartek Kuźma | Artur Błażejewski | Zuzanna Bordzicka | Paweł Bujnowski | Klaudia Firlag | Christian Goltz | Maciej Grabowski | Jakub Jończyk | Grzegorz Kłosiński | Bartłomiej Paziewski | Natalia Paszkiewicz | Jarosław Piersa | Piotr Andruszkiewicz
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

In this work we present an overview of our winning system for the R2VQ - Competence-based Multimodal Question Answering task, with the final exact match score of 92.53%.The task is structured as question-answer pairs, querying how well a system is capable of competence-based comprehension of recipes. We propose a hybrid of a rule-based system, Question Answering Transformer, and a neural classifier for N/A answers recognition. The rule-based system focuses on intent identification, data extraction and response generation.


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SRPOL DIALOGUE SYSTEMS at SemEval-2021 Task 5: Automatic Generation of Training Data for Toxic Spans Detection
Michał Satława | Katarzyna Zamłyńska | Jarosław Piersa | Joanna Kolis | Klaudia Firląg | Katarzyna Beksa | Zuzanna Bordzicka | Christian Goltz | Paweł Bujnowski | Piotr Andruszkiewicz
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper presents a system used for SemEval-2021 Task 5: Toxic Spans Detection. Our system is an ensemble of BERT-based models for binary word classification, trained on a dataset extended by toxic comments modified and generated by two language models. For the toxic word classification, the prediction threshold value was optimized separately for every comment, in order to maximize the expected F1 value.