Augmenting Data with Mixup for Sentence Classification: An Empirical Study
Table 2: Accuracy (%) of the testing methods using CNN (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']
RandomTune has the largest number of parameters, compared to RandomFix, PretrainTune, and PretrainFix, and thus requires a strong regularization method to avoid over-fit the training data. We, therefore, focus our experiments on the RandomTune setting. For example, in SST-1 and MR, the relative improvement was over 3.3%. Interestingly, both wordMixup and senMixup failed to significantly improved over the baseline against the SST-2 dataset; with senMixup slightly outperformed the baseline with only 0.7%. For example, on the Trec dataset, senMixup outperformed senMixup with 1.2%, but for the other four datasets, the two methods obtained very similiar predictive accuracy.