Improving RNN-based Answer Selection for Morphologically Rich Languages
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F20%3A00114091" target="_blank" >RIV/00216224:14330/20:00114091 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.5220/0008979206440651" target="_blank" >http://dx.doi.org/10.5220/0008979206440651</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0008979206440651" target="_blank" >10.5220/0008979206440651</a>
Alternative languages
Result language
angličtina
Original language name
Improving RNN-based Answer Selection for Morphologically Rich Languages
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/GA18-23891S" target="_blank" >GA18-23891S: Hyperintensional Reasoning over Natural Language Texts</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 12th International Conference on Agents and Artificial Intelligence
ISBN
9789897583957
ISSN
2184-433X
e-ISSN
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Number of pages
8
Pages from-to
644-651
Publisher name
SCITEPRESS
Place of publication
Portugal
Event location
Portugal
Event date
Jan 1, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000570769000069