TraceNet: Tracing and Locating the Key Elements in Sentiment Analysis
This work addresses interpretability and robustness in sentiment analysis for NLP applications, though it appears incremental as it builds on existing two-streams hypothesis and neural methods.
The paper tackles sentiment analysis by identifying key input elements that influence outcomes, proposing TraceNet, a neural architecture that simultaneously learns representations and traces these elements, achieving effective sentiment classification with demonstrated robustness and interpretability.
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.