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@inproceedings{1458718,
author = {Víta, Martin},
address = {Brno},
booktitle = {Siamese Convolutional Neural Networks for Recognizing Partial Entailment},
editor = {J. Zendulka, M. Bieliková, R. Burget, Z. Křivka (eds.)},
keywords = {Partial Textual Entailment; Convolutional Neural Networks; Siamese Architectures},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Brno},
isbn = {978-80-214-5679-2},
pages = {237-242},
publisher = {Vysoké učení technické v Brně},
title = {Siamese Convolutional Neural Networks for Recognizing Partial Entailment},
url = {http://daz2018.fit.vutbr.cz/DaZ_WIKT_2018_Sbornik.pdf},
year = {2018}
}
TY - JOUR
ID - 1458718
AU - Víta, Martin
PY - 2018
TI - Siamese Convolutional Neural Networks for Recognizing Partial Entailment
PB - Vysoké učení technické v Brně
CY - Brno
SN - 9788021456792
KW - Partial Textual Entailment
KW - Convolutional Neural Networks
KW - Siamese Architectures
UR - http://daz2018.fit.vutbr.cz/DaZ_WIKT_2018_Sbornik.pdf
L2 - http://daz2018.fit.vutbr.cz/DaZ_WIKT_2018_Sbornik.pdf
N2 - Recognizing textual entailment (RTE), i. e., a decision problem whether a sentence (called hypothesis) can be inferred from a given text, became a well established and widely studied task. As a consequence of the traditional binary (or ternary) class formulation, it is not possible to express the fact that a fragment of the hypothesis is entailed by the text, even though the “whole” entailment of the hypothesis from the text does not hold. The notions of partial textual entailment – and faceted entailment in particular – address this problem. In this paper, we introduce a siamese CNN architecture with a static attention mechanism together with a sentence compression and provide an evaluation over modified SemEval 2013 Task 8 dataset.
ER -
VÍTA, Martin. Siamese Convolutional Neural Networks for Recognizing Partial Entailment. Online. In J. Zendulka, M. Bieliková, R. Burget, Z. Křivka (eds.). \textit{Siamese Convolutional Neural Networks for Recognizing Partial Entailment}. Brno: Vysoké učení technické v Brně, 2018, s.~237-242. ISBN~978-80-214-5679-2.
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