  Many NLP learning tasks can be decomposed into several distinct sub-tasks,
each associated with a partial label. In this paper we focus on a popular class
of learning problems, sequence prediction applied to several sentiment analysis
tasks, and suggest a modular learning approach in which different sub-tasks are
learned using separate functional modules, combined to perform the final task
while sharing information. Our experiments show this approach helps constrain
the learning process and can alleviate some of the supervision efforts.
