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Importance of Self Attention for Sentiment Analysis

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Figure 1: Our Self-Attention Network (SANet),derivedfromtheTransformerarchitec-ture (Vaswani et al., 2017).The self-attentionblock is repeated N times.
Figure 2: Visualization of sequences length distributions. For each dataset, the total number of exam-ples is presented on the right and task semantics are identified on the left: Topic Classification (TC) orSentiment Analysis (SA).
Table 1: Test error rates (%) for text classification. In bold, the state-of-the-art and in italic, our bestmodel
Figure 3: Self-attention different behavior for each text classification task. The attention matrices wereextracted from the SANet base model applied on the testing set of each dataset
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