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Siamese Convolutional Neural Networks for Recognizing Partial Entailment

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VÍTA Martin

Rok publikování 2018
Druh Článek ve sborníku
Konference Siamese Convolutional Neural Networks for Recognizing Partial Entailment
Fakulta / Pracoviště MU

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Klíčová slova Partial Textual Entailment; Convolutional Neural Networks; Siamese Architectures
Popis 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.
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