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INTERACT focuses on developing innovative learning algorithms for Natural Language Understanding by minimizing human feedback through a collaborative machine learning approach. It aims to enhance the efficiency of learning complex language structures.
INTERACT will develop new Interactive Learning Algorithms (ILA), motivated by applications in Natural Language Understanding (NLU).
The main assumptions behind supervised approaches are unrealistic because most NLU applications have unique information needs, and large collections of annotated data are necessary to achieve good performance. INTERACT follows a collaborative machine learning paradigm that breaks the distinction between annotation and training.
We focus on compositional latent-state…
UNIVERSITAT POLITECNICA DE CATALUNYA
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