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Learning Disentangled Representations of Texts with Application to Biomedical Abstracts

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Figure

Figure 2: Schematic of our encoder architecture.
Table 1: AUCs achieved using different representations on the Cohen et al. corpus. Models to the rightof the | are supervised; those to the right of || constitute the proposed disentangled embeddings.
Table 7: ‘Decorrelated’ cross-AUC results on theBeerAdvocate data, which attempt to mitigateconfounding due to overall sentiment being cor-related
Table 9: Gate activations for each aspect in an example beer review.

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