On Controlled DeEntanglement for Natural Language Processing
  Latest addition to the toolbox of human species is Artificial
Intelligence(AI). Thus far, AI has made significant progress in low stake low
risk scenarios such as playing Go and we are currently in a transition toward
medium stake scenarios such as Visual Dialog. In my thesis, I argue that we
need to incorporate controlled de-entanglement as first class object to succeed
in this transition. I present mathematical analysis from information theory to
show that employing stochasticity leads to controlled de-entanglement of
relevant factors of variation at various levels. Based on this, I highlight
results from initial experiments that depict efficacy of the proposed
framework. I conclude this writeup by a roadmap of experiments that show the
applicability of this framework to scalability, flexibility and
interpretibility.
