English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper, we study sentiment analysis task where the outcomes are mainlycontributed by a few key elements of the inputs. Motivated by the two-streamshypothesis, we propose a neural architecture, named TraceNet, to address thistype of task. It not only learns discriminative representations for the targettask via its encoders, but also traces key elements at the same time via itslocators. In TraceNet, both encoders and locators are organized in a layer-wisemanner, and a smoothness regularization is employed between adjacentencoder-locator combinations. Moreover, a sparsity constraints are enforced onlocators for tracing purposes and items are proactively masked according to theitem weights output by locators.A major advantage of TraceNet is that theoutcomes are easier to understand, since the most responsible parts of inputsare identified. Also, under the guidance of locators, it is more robust toattacks due to its focus on key elements and the proactive masking trainingstrategy. Experimental results show its effectiveness for sentimentclassification. Moreover, we provide several case studies to demonstrate itsrobustness and interpretability.

TraceNet: Tracing and Locating the Key Elements in Sentiment Analysis