Comparing RNN and Transformer Context Representations in the Czech Answer Selection Task

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00125094" target="_blank" >RIV/00216224:14330/22:00125094 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.5220/0000155600003116" target="_blank" >http://dx.doi.org/10.5220/0000155600003116</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0000155600003116" target="_blank" >10.5220/0000155600003116</a>

Result language
angličtina
Original language name
Comparing RNN and Transformer Context Representations in the Czech Answer Selection Task
Original language description
Open domain question answering now inevitably builds upon advanced neural models processing large unstructured textual sources serving as a kind of underlying knowledge base. In case of non-mainstream highly- inflected languages, the state-of-the-art approaches lack large training datasets emphasizing the need for other improvement techniques. In this paper, we present detailed evaluation of a new technique employing various context representations in the answer selection task where the best answer sentence from a candidate document is identified as the most relevant to the human entered question. The input data here consists not only of each sentence in isolation but also of its preceding sentence(s) as the context. We compare seven different context representations including direct recurrent network (RNN) embeddings and several BERT-model based sentence embedding vectors. All experiments are evaluated with a new version 3.1 of the Czech question answering benchmark dataset SQAD wit h possible multiple correct answers as a new feature. The comparison shows that the BERT-based sentence embeddings are able to offer the best context representations reaching the mean average precision results of 83.39% which is a new best score for this dataset.
Czech name
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Czech description
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Project
<a href="/en/project/LM2018101" target="_blank" >LM2018101: Digital Research Infrastructure for the Language Technologies, Arts and Humanities</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 14th International Conference on Agents and Artificial Intelligence (ICAART)
ISBN
9789897585470
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
388-394
Publisher name
SCITEPRESS
Place of publication
Portugal
Event location
Portugal
Event date
Jan 1, 2022
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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