Rethinking Text Attribute Transfer: A Lexical Analysis
Table 2: Classification accuracy. The voting based pivot classifier is a strong classification baseline compared with the state of art CNN classifier, indicating that in many datasets, words are strong features for class labels.
['[BOLD] Validation', 'Yelp', 'Amazon', 'Caption', 'Gender', 'Paper', 'Politics', 'Reddit', 'Twitter']
Also, we only consider unigrams(words), while it is also straightforward to extend it to ngrams. As for the parameters in the algorithm, the precision threshold p0 controls the confidence of a word to be a pivot, and the occurrence threshold f0 prevents overfitting. We tune these parameters based on the classification performance on the validation set. Specifically, to get better classification performance, f0 and p0 should be lower to allow more vote (e.g. f0≤10,p0∈[0.5,0.7]). To get more confidence and filter out stronger pivot words, f0 and p0 should be higher (e.g. f0≥100,p0≥0.7). We have balanced the test sets so the random baseline is 50%. This voting based classifier achieves comparable performance with the two models in 4 datasets (Amazon, Gender, Paper, Politics), and only loses small margins in 2 datasets (Yelp, Caption). Although the independence assumption from our pivot classifier does not necessarily hold for all datasets, empirically it performs very well. This means that these pivot words are a meaningful approximation of the true decision boundary.