Abstract
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.