Hierarchical Coherence Modeling for Document Quality Assessment

Authors

  • Dongliang Liao Data Quality Team WeChat Tencent Inc. China
  • Jin Xu Data Quality Team WeChat Tencent Inc. China
  • Gongfu Li Data Quality Team WeChat Tencent Inc. China
  • Yiru Wang Data Quality Team WeChat Tencent Inc. China

Keywords:

Text Classification & Sentiment Analysis

Abstract

Text coherence plays a key role in document quality assessment. Most existing text coherence methods only focus on similarity of adjacent sentences. However, local coherence exists in sentences with broader contexts and diverse rhetoric relations, rather than just adjacent sentences similarity. Besides, the highlevel 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 a local attention mechanism to capture the location semantics, bilinear tensor layer for measure coherence and max-coherence pooling for acquiring high-level 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.

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Published

2021-05-18

How to Cite

Liao, D., Xu, J., Li, G., & Wang, Y. (2021). Hierarchical Coherence Modeling for Document Quality Assessment. Proceedings of the AAAI Conference on Artificial Intelligence, 35(15), 13353-13361. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17576

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing II