Siamese Convolutional Neural Networks for Recognizing Partial Entailment

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Authors	VÍTA Martin
Year of publication	2018
Type	Article in Proceedings
Conference	Siamese Convolutional Neural Networks for Recognizing Partial Entailment
MU Faculty or unit	Faculty of Informatics
Citation
Web	Full paper
Keywords	Partial Textual Entailment; Convolutional Neural Networks; Siamese Architectures
Description	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.
Related projects:	Rozsáhlé výpočetní systémy: modely, aplikace a verifikace VII.