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Rationale Augmented Convolutional Neural Networks for Text Classification

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Academic year: 2020

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Figure

Figure 1: A toy example of a CNN for sentenceclassification. Here there are four filters, two withheights 2 and two with heights 3, resulting in featuremaps with lengths 6 and 5 respectively.
Figure 2: A schematic of our proposed Rationale-Augmented Convolution Neural Network (RA-CNN).The sentences comprising a text are passed through a sentence model that outputs probabilities encoding thelikelihood that sentences are neutral or a (positive or
Table 1: Dataset characteristics. N is the number ofinstances, #sen is the average sentence count, #tokenis the average token per-sentence count and #rat isthe average number of rationales per document.
Table 2: Accuracies on the four RoB datasets. Uni-SVM: unigram SVM, Bi-SVM: Bigram SVM, RA-SVM:Rationale-augmented SVM (Zaidan et al., 2007), MT-SVM: a multi-task SVM model specifically designedfor the RoB task, which also exploits the available sentence su

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