Learning Disentangled Representations of Texts with Application to Biomedical Abstracts
Table 6: Cross AUC results for different representations on the BeerAdvocate data. Row: Embedding used. Column: Aspect evaluated against.
['[EMPTY]', 'Look', 'Aroma', 'Palate', 'Taste']
Rows correspond to the embedding used and columns to the aspect evaluated against. As expected, aspect-embeddings perform better w.r.t. the aspects for which they code, suggesting some disentanglement. However, the reduction in performance when using one aspect representation to discriminate w.r.t. others is not as pronounced as above. This is because aspect ratings are highly correlated: if taste is positive, aroma is very likely to be as well. Effectively, here sentiment entangles all of these aspects.