![]() ![]() ![]() Zhou, Hierarchical modeling of global context for document-level neural machine translation, in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), ACL, Hong Kong, China, (2019), 1576–1585. Strube, Centering-based neural coherence modeling with hierarchical discourse segments, in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), ACL, Online, (2020), 7458–7472. Sanriya, A hybrid approach towards automated essay evaluation based on BERT and feature engineering, in 2022 IEEE 7th International Conference for Convergence in Technology (I2CT), IEEE, Vadodara, India, (2022), 1–4. Sentence coherence evaluation based on neural network and textual features for official documents. This result is significantly better than the previous best method, proving the superiority of our approach in solving this problem.Ĭitation: Yunmei Shi, Yuanhua Li, Ning Li. Experiments were conducted on official documents dataset and THUCNews public dataset, our method has achieved an averaged 3.8% improvement in accuracy indicator compared to past research, reaching a 96.2% accuracy rate. To ensure that the automatically generated official documents are coherent, we propose a sentence coherence evaluation model integrating repetitive words features, which introduces repetitive words features with neural network-based approach for the first time. With the development of e-government, automatic generation of official documents can significantly reduce the writing burden of government agencies. Sentence coherence is an essential foundation for discourse coherence in natural language processing, as it plays a vital role in enhancing language expression, text readability, and improving the quality of written documents. ![]()
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