Beyond End-to-end: Decomposed Modeling and Representations in NLP

ABSTRACT: Deep learning has pushed us to develop models and representations which we understand and control to a much lesser degree than earlier methods. In this talk I suggest revisiting the pursuit of decomposed models and representations, aiming to regain a better understanding and control of our systems while still leveraging deep learning power. With respect to modeling, I will discuss an alternative to the common end-to-end approach, where a complex task is decomposed to its inherent subtasks, each addressed …

modeling nlp