Improving RNN-based Answer Selection for Morphologically Rich
Languages

D 2020

Improving RNN-based Answer Selection for Morphologically Rich Languages

MEDVEĎ, Marek, Aleš HORÁK a Radoslav SABOL

Základní údaje

Originální název

Improving RNN-based Answer Selection for Morphologically Rich Languages

Autoři

MEDVEĎ, Marek (703 Slovensko, domácí), Aleš HORÁK (203 Česká republika, garant, domácí) a Radoslav SABOL (703 Slovensko, domácí)

Vydání

Portugal, Proceedings of the 12th International Conference on Agents and Artificial Intelligence, od s. 644-651, 8 s. 2020

Nakladatel

SCITEPRESS

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Portugalsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14330/20:00114091

Organizační jednotka

Fakulta informatiky

ISBN

978-989-758-395-7

ISSN

DOI

http://dx.doi.org/10.5220/0008979206440651

UT WoS

000570769000069

Klíčová slova anglicky

Question Answering; Question Classification; Answer Classification; Czech; Simple Question Answering Database; SQAD

Štítky

firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 28. 4. 2021 18:01, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

Question answering systems have improved greatly during the last five years by employing architectures of deep neural networks such as attentive recurrent networks or transformer-based networks with pretrained con- textual information. In this paper, we present the results and detailed analysis of experiments with the largest question answering benchmark dataset for the Czech language. The best results evaluated in the text reach the accuracy of 72 %, which is a 4 % improvement to the previous best result. We also introduce the newest version of the Czech Question Answering benchmark dataset SQAD 3.0, which was substantially extended to more than 13,000 question-answer pairs, and we report the first answer selection results on this dataset which indicate that the size of the training data is important for the task.

Návaznosti

GA18-23891S, projekt VaV

Název: Hyperintensionální usuzování nad texty přirozeného jazyka

Investor: Grantová agentura ČR, Hyperintensionální usuzování nad texty přirozeného jazyka

Citovat

MEDVEĎ, Marek, Aleš HORÁK a Radoslav SABOL. Improving RNN-based Answer Selection for Morphologically Rich Languages. Online. In Ana Rocha, Luc Steels, Jaap van den Herik. Proceedings of the 12th International Conference on Agents and Artificial Intelligence. Portugal: SCITEPRESS, 2020, s. 644-651. ISBN 978-989-758-395-7. Dostupné z: https://dx.doi.org/10.5220/0008979206440651.

@inproceedings{1643878,
   author = {Medveď, Marek and Horák, Aleš and Sabol, Radoslav},
   address = {Portugal},
   booktitle = {Proceedings of the 12th International Conference on Agents and Artificial Intelligence},
   doi = {http://dx.doi.org/10.5220/0008979206440651},
   editor = {Ana Rocha, Luc Steels, Jaap van den Herik},
   keywords = {Question Answering; Question Classification; Answer Classification; Czech; Simple Question Answering Database; SQAD},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Portugal},
   isbn = {978-989-758-395-7},
   pages = {644-651},
   publisher = {SCITEPRESS},
   title = {Improving RNN-based Answer Selection for Morphologically Rich Languages},
   year = {2020}
}

TY  - JOUR
ID  - 1643878
AU  - Medveď, Marek - Horák, Aleš - Sabol, Radoslav
PY  - 2020
TI  - Improving RNN-based Answer Selection for Morphologically Rich Languages
PB  - SCITEPRESS
CY  - Portugal
SN  - 9789897583957
KW  - Question Answering
KW  - Question Classification
KW  - Answer Classification
KW  - Czech
KW  - Simple Question Answering Database
KW  - SQAD
N2  - Question answering systems have improved greatly during the last five years by employing architectures of deep neural networks such as attentive recurrent networks or transformer-based networks with pretrained con- textual information. In this paper, we present the results and detailed analysis of experiments with the largest question answering benchmark dataset for the Czech language. The best results evaluated in the text reach the accuracy of 72 %, which is a 4 % improvement to the previous best result. We also introduce the newest version of the Czech Question Answering benchmark dataset SQAD 3.0, which was substantially extended to more than 13,000 question-answer pairs, and we report the first answer selection results on this dataset which indicate that the size of the training data is important for the task.
ER  -

MEDVEĎ, Marek, Aleš HORÁK a Radoslav SABOL. Improving RNN-based Answer Selection for Morphologically Rich Languages. Online. In Ana Rocha, Luc Steels, Jaap van den Herik. \textit{Proceedings of the 12th International Conference on Agents and Artificial Intelligence}. Portugal: SCITEPRESS, 2020, s.~644-651. ISBN~978-989-758-395-7. Dostupné z: https://dx.doi.org/10.5220/0008979206440651.

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