Klaudia Firląg

Also published as: Klaudia Firlag


2022

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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.

2021

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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.

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Multilingual Entity and Relation Extraction Dataset and Model
Alessandro Seganti | Klaudia Firląg | Helena Skowronska | Michał Satława | Piotr Andruszkiewicz
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

We present a novel dataset and model for a multilingual setting to approach the task of Joint Entity and Relation Extraction. The SMiLER dataset consists of 1.1 M annotated sentences, representing 36 relations, and 14 languages. To the best of our knowledge, this is currently both the largest and the most comprehensive dataset of this type. We introduce HERBERTa, a pipeline that combines two independent BERT models: one for sequence classification, and the other for entity tagging. The model achieves micro F1 81.49 for English on this dataset, which is close to the current SOTA on CoNLL, SpERT.