Tomasz Dryjański


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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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VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization
Hyungtak Choi | Lohith Ravuru | Tomasz Dryjański | Sunghan Rye | Donghyun Lee | Hojung Lee | Inchul Hwang
Proceedings of the 12th International Conference on Natural Language Generation

This paper describes our submission to the TL;DR challenge. Neural abstractive summarization models have been successful in generating fluent and consistent summaries with advancements like the copy (Pointer-generator) and coverage mechanisms. However, these models suffer from their extractive nature as they learn to copy words from the source text. In this paper, we propose a novel abstractive model based on Variational Autoencoder (VAE) to address this issue. We also propose a Unified Summarization Framework for the generation of summaries. Our model eliminates non-critical information at a sentence-level with an extractive summarization module and generates the summary word by word using an abstractive summarization module. To implement our framework, we combine submodules with state-of-the-art techniques including Pointer-Generator Network (PGN) and BERT while also using our new VAE-PGN abstractive model. We evaluate our model on the benchmark Reddit corpus as part of the TL;DR challenge and show that our model outperforms the baseline in ROUGE score while generating diverse summaries.