  In this paper, we study sentiment analysis task where the outcomes are mainly
contributed by a few key elements of the inputs. Motivated by the two-streams
hypothesis, we propose a neural architecture, named TraceNet, to address this
type of task. It not only learns discriminative representations for the target
task via its encoders, but also traces key elements at the same time via its
locators. In TraceNet, both encoders and locators are organized in a layer-wise
manner, and a smoothness regularization is employed between adjacent
encoder-locator combinations. Moreover, a sparsity constraints are enforced on
locators for tracing purposes and items are proactively masked according to the
item weights output by locators.A major advantage of TraceNet is that the
outcomes are easier to understand, since the most responsible parts of inputs
are identified. Also, under the guidance of locators, it is more robust to
attacks due to its focus on key elements and the proactive masking training
strategy. Experimental results show its effectiveness for sentiment
classification. Moreover, we provide several case studies to demonstrate its
robustness and interpretability.
