  Recognizing emotions using few attribute dimensions such as arousal, valence
and dominance provides the flexibility to effectively represent complex range
of emotional behaviors. Conventional methods to learn these emotional
descriptors primarily focus on separate models to recognize each of these
attributes. Recent work has shown that learning these attributes together
regularizes the models, leading to better feature representations. This study
explores new forms of regularization by adding unsupervised auxiliary tasks to
reconstruct hidden layer representations. This auxiliary task requires the
denoising of hidden representations at every layer of an auto-encoder. The
framework relies on ladder networks that utilize skip connections between
encoder and decoder layers to learn powerful representations of emotional
dimensions. The results show that ladder networks improve the performance of
the system compared to baselines that individually learn each attribute, and
conventional denoising autoencoders. Furthermore, the unsupervised auxiliary
tasks have promising potential to be used in a semi-supervised setting, where
few labeled sentences are available.
