TraceNet: Tracing and Locating the Key Elements in Sentiment Analysis

TL;DR

TraceNet is a neural architecture designed for sentiment analysis that identifies and traces key input elements, improving interpretability and robustness by focusing on important features and masking less relevant parts.

Contribution

The paper introduces TraceNet, a novel neural model with locators and encoders organized layer-wise, employing regularization and sparsity constraints for better interpretability and robustness in sentiment analysis.

Findings

Effective in sentiment classification tasks

Enhances interpretability by tracing key input elements

Improves robustness through proactive masking

Abstract

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.