Samsung Research Poland (SRPOL) at SemEval-2022 Task 9: Hybrid Question Answering Using Semantic Roles

Tomasz Dryjanski, Monika Zaleska, Bartek Kuzma, Artur Blazejewski, Zuzanna Bordzicka, Pawel Bujnowski, Klaudia Firlag, Christian Goltz, Maciej Grabowski, Jakub Jonczyk, Grzegorz Klosinski, Bartlomiej Paziewski, Natalia Paszkiewicz, Jaroslaw Piersa, Piotr Andruszkiewicz

Abstract: 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.