  In this paper we study test time decoding; an ubiquitous step in almost all
sequential text generation task spanning across a wide array of natural
language processing (NLP) problems. Our main contribution is to develop a
continuous relaxation framework for the combinatorial NP-hard decoding problem
and propose Disco - an efficient algorithm based on standard first order
gradient based. We provide tight analysis and show that our proposed algorithm
linearly converges to within $\epsilon$ neighborhood of the optima. Finally, we
perform preliminary experiments on the task of adversarial text generation and
show superior performance of Disco over several popular decoding approaches.

Computation and Language;Artificial Intelligence;Machine Learning