Hierarchical Coherence Modeling for Document Quality Assessment

Text coherence plays a key role in document quality assessment. Most existing text coherence methods only focus on the similarity of adjacent sentences. However, local coherence exists in sentences with broader contexts and diverse rhetoric relations, rather than just adjacent sentence similarity. Besides, the high-level text coherence is also an important aspect of document quality. To this end, we propose a hierarchical coherence model for document quality assessment. In our model, we implement the local attention mechanism to capture the location semantics, bilinear tensor layer to measure coherence and max-coherence pooling to acquire highlevel coherence. We evaluate the proposed method on two realistic tasks: news quality judgement and automated essay scoring. Experimental results demonstrate the validity and superiority of our work.