SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring

Understand

Deep learning has demonstrated tremendous potential for Automatic Text Scoring (ATS) tasks.

• In this paper, we describe a new neural architecture that enhances vanilla neural network models with auxiliary neural coherence features.
• Our new method proposes a new \textsc{SkipFlow} mechanism that models relationships between snapshots of the hidden representations of a long short-term memory (LSTM) network as it reads.
• Subsequently, the semantic relationships between multiple snapshots are used as auxiliary features for prediction.