SABOL, Radoslav, Marek MEDVEĎ and Aleš HORÁK. Recurrent Networks in AQA Answer Selection. In Aleš Horák, Pavel Rychlý and Adam Rambousek. Proceedings of the Twelfth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2018. Brno: Tribun EU, 2018, p. 53-62. ISBN 978-80-263-1517-9.
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Basic information
Original name Recurrent Networks in AQA Answer Selection
Authors SABOL, Radoslav (703 Slovakia, belonging to the institution), Marek MEDVEĎ (703 Slovakia, belonging to the institution) and Aleš HORÁK (203 Czech Republic, guarantor, belonging to the institution).
Edition Brno, Proceedings of the Twelfth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2018, p. 53-62, 10 pp. 2018.
Publisher Tribun EU
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10200 1.2 Computer and information sciences
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW URL
RIV identification code RIV/00216224:14330/18:00101541
Organization unit Faculty of Informatics
ISBN 978-80-263-1517-9
ISSN 2336-4289
UT WoS 000612420300007
Keywords in English question answering; answer selection; QA dataset; SQAD; AQA
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 20/9/2022 11:11.
Abstract
Unlimited, or open domain, question answering system AQA is being developed and tested with the Simple Question Answering Data-base (SQAD) for the Czech language. AQA is optimized for work with morphologically rich languages and makes use of syntactic cues provided by the morphosyntactic analysis. In this paper, we introduce a new answer selection module being developed for the AQA system. The module is based on recurrent neural networks processing the question and answer sentences to derive the most probable answer sentence. We present the details of the module architecture and offer a detailed evaluation of various hyperparameter setups. The module is trained and tested with 8,500 question-answer pairs using the SQAD v2.1 benchmark dataset.
Links
GA18-23891S, research and development projectName: Hyperintensionální usuzování nad texty přirozeného jazyka
Investor: Czech Science Foundation
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