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.
- Tomasz Dryjański 1
- Monika Zaleska 1
- Bartek Kuźma 1
- Artur Błażejewski 1
- Zuzanna Bordzicka 1
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