Learning Disentangled Representations of Texts with Application to Biomedical Abstracts
Table 1: AUCs achieved using different representations on the Cohen et al. corpus. Models to the right of the | are supervised; those to the right of || constitute the proposed disentangled embeddings.
['Study', 'TF-IDF', 'Doc2Vec', 'LDA', 'NVDM', 'ABAE', 'RR-TF', 'DSSM', '[BOLD] P', '[BOLD] I', '[BOLD] O', '[BOLD] [P|I|O]']
Results. In brief: The proposed PICO embeddings (concatenated) obtain an equivalent or higher AUC than baseline strategies on 12/14 reviews, and strictly higher AUCs in 11/14. It is unsurprising that we outperform unsupervised approaches, but we also best RR-TF, which was trained with the same CDSR corpus Wallace et al. DSSM Shen et al. We outperform the latter by an average performance gain of 4 points AUC (significant at 95% level using independent 2-sample t-test).