Learning Disentangled Representations of Texts with Application to Biomedical Abstracts
Table 7: ‘Decorrelated’ cross-AUC results on the BeerAdvocate data, which attempt to mitigate confounding due to overall sentiment being correlated. Each cell reports metrics over subsets of reviews in which the sentiment differs between the row and column aspects. The numbers in each cell are the AUCs w.r.t. sentiment regarding the column aspect achieved using the row and column aspect representations, respectively.
['Look', 'Look -', 'Look -', 'Aroma 0.42', 'Aroma 0.60', 'Palate 0.40', 'Palate 0.63', 'Taste 0.38', 'Taste 0.65']
Specifically, for each cell (k,k′) in the table, we first retrieve the subset of reviews in which the sentiment w.r.t. k differs from the sentiment w.r.t. k′. Then we evaluate the AUC similarity of these reviews on the basis of sentiment concerning k′ using both k and k′ embeddings, yielding a pair of AUCs (listed respectively). We observe that the using k′ embeddings to evaluate aspect k′ similarity yields better results than using k embeddings.