Augmenting Data with Mixup for Sentence Classification: An Empirical Study
Table 3: Accuracy (%) obtained by the testing methods using LSTM (with randomly initialized, trainable embeddings). We report mean scores over 10 runs with standard deviations (denoted ±). Best results highlighted in Bold.
['[BOLD] RandomTune', 'Trec', 'SST-1', 'SST-2', 'Subj', 'MR']
LSTM Networks as Sentence Encoder We also evaluate the effect of using LSTM as the sentence encoder. For example, the largest improvements came from the Trec and SST-1 cases (with relative improvement of 4.62% and 5.22%), which have six and five classes, respectively. Results in the table also suggest that, on the Subj dataset, wordMixup outperformed senMixup with 1.2%, but for the other four datasets, the two methods performed comparably well.