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Recognizing emotions using few attribute dimensions such as arousal, valenceand dominance provides the flexibility to effectively represent complex rangeof emotional behaviors. Conventional methods to learn these emotionaldescriptors primarily focus on separate models to recognize each of theseattributes. Recent work has shown that learning these attributes togetherregularizes the models, leading to better feature representations. This studyexplores new forms of regularization by adding unsupervised auxiliary tasks toreconstruct hidden layer representations. This auxiliary task requires thedenoising of hidden representations at every layer of an auto-encoder. Theframework relies on ladder networks that utilize skip connections betweenencoder and decoder layers to learn powerful representations of emotionaldimensions. The results show that ladder networks improve the performance ofthe system compared to baselines that individually learn each attribute, andconventional denoising autoencoders. Furthermore, the unsupervised auxiliarytasks have promising potential to be used in a semi-supervised setting, wherefew labeled sentences are available.

Ladder Networks for Emotion Recognition: Using Unsupervised Auxiliary Tasks to Improve Predictions of Emotional Attributes