Updating Pre-trained Word Vectors and Text Classifiers using Monolingual Alignment
Table 3: Comparison between the accuracy of our aligned model (RCSLS+Fine.), the accuracy of models trained on each split separately (Train on …) and two baselines. We observe that a model obtained using our procedure matches the performance of the Vote baseline while not requiring to store two separate models.
['[EMPTY]', 'Sogou 3k', 'Sogou 30k', 'Sogou Full', 'Amazon 3k', 'Amazon 30k', 'Amazon Full', 'Yelp 3k', 'Yelp 30k', 'Yelp Full']
We report the performance of a model trained on each of the two splits alone, the two baselines, and a topline obtained by training on S0∪S1. First of all, we observe that our approach reduces the gap between training on a single set with the topline. This effect is especially true on small versions of the datasets (3k or 30k). The main reason for this improvement is that each split has an incomplete coverage of the discriminative words and n-grams.