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Phrase level Self Attention Networks for Universal Sentence Encoding

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Figure

Figure 1: An example of phrase division, the sentence and its parse tree are from the SNLI training data.The division is started from the root of a parse tree
Table 2: Performance on SNLI and transfer tasks ofeters. Test accuracies on SNLI, micro and macroaverages of accuracies of dev set on transfer tasksvarious sentence encoders
Table 3: Transfer test results for our model and various baselines. Classification accuracy is chosen asevaluation metric for datasets including MR, CR, SUBJ, MPQA, SST, TREC and SICK-E; Classificationaccuracy and F1-score are chosen for MRPC; Pearson correlation is chosen for SICK-R; Pearson andSpearman correlations are chosen for STS-14.
Table 5: Memory consumption and test accuracyof three fully attention-based models on the TRECdataset.
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