Multi-Task Unsupervised Contextual Learning for Behavioral Annotation
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Unsupervised learning has been an attractive method for easily deriving meaningful data representations from vast amounts of unlabeled data. These representations or, in the context of deep learning, embeddings, often yield superior results in many tasks, whether used directly or as features in subsequent training stages. However, the quality of the embeddings is highly dependent on the assumed knowledge in the unlabeled data and how the system extracts information without supervision. Domain portability is also very limited in unsupervised learning, often requiring re-training on other large-scale corpora to achieve robustness. In this work we present a paradigm for unsupervised contextual learning for behavioral interactions which addresses unsupervised domain adaption. We introduce a multitask ob- jective into unsupervised learning and show that embeddings generated through this process increases performance of behavior related tasks.
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