论文标题
TraceNet：在情感分析中追踪和定位关键要素
TraceNet: Tracing and Locating the Key Elements in Sentiment Analysis
论文作者
论文摘要
在本文中，我们研究了情感分析任务，其中结果主要由输入的一些关键要素贡献。我们提出了一个名为TraceNet的神经架构，以解决这类任务。它不仅通过其编码器学习目标任务的歧视性表示，而且还通过其定位器同时跟踪关键元素。在TraceNet中，编码器和定位器都是以层次的方式组织的，并且在相邻编码器组合之间采用平滑度正则化。此外，在定位器上强制执行稀疏性约束以进行追踪，并根据定位器的项目权重主动掩盖了项目。traceNet的主要优势是，结果更容易理解，因为确定了输入的最负责任的部分。同样，在定位者的指导下，由于关注关键要素和积极主动的掩盖培训策略，攻击更为强大。实验结果表明其对情感分类的有效性。此外，我们提供了几个案例研究，以证明其鲁棒性和解释性。
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.
