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Happiness Ingredients Detection using Multi-Task Deep Learning

We propose a novel deep multi-task learning model for the task of detecting happiness ingredients. The two classes/labels ”agency” and ”social” are treated as two separate tasks for training Deep Learning classifiers. Then, we train a multi-task deep learning classifier to see if the shared knowledge between the two tasks can improve the overall results. In addition, we compare several models that use different kinds of word embeddings: different dimensions of the vectors, fixed versus trainable embeddings, initialized randomly or with existing embeddings.