Publication details
Siamese Convolutional Neural Networks for Recognizing Partial Entailment
| Authors | |
|---|---|
| Year of publication | 2018 |
| Type | Article in Proceedings |
| Conference | Siamese Convolutional Neural Networks for Recognizing Partial Entailment |
| MU Faculty or unit | |
| 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. |
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