Tomasz Dryjański
2022
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.
2019
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.
Search
Co-authors
- Hyungtak Choi 1
- Lohith Ravuru 1
- Sunghan Rye 1
- Donghyun Lee 1
- Hojung Lee 1
- show all...