Snippext: Semi-supervised Opinion Mining with Augmented Data
Table 4. Results on 1,000 samples and full training sets.
['Methods', 'AE@1000 rest', 'AE@1000 laptop', 'ASC@1000 rest', 'ASC@1000 laptop', 'AE@full rest', 'AE@full laptop', 'ASC@full rest', 'ASC@full laptop']
Low resource setting. In restaurant AE and ASC, MixMatchNL already outperforms BERT-PT−, which is trained with the full data, using only 1,000 labeled training examples, i.e., 50% and 28% of training examples respectively. MixDA also achieves similar good performance with only 1000 size training set on restaurant AE and even outperforms BERT-PT on full training data by 0.9% (77.79-76.9) on the restaurant ASC task. In laptop AE and ASC, MixMatchNL achieves results within 2.07% and 0.04% to BERT-PT− using 33% or 43% of training examples respectively. These results indicate that the proposed methods are able to significantly reduce the number of training labels required for opinion mining tasks.