基于论证关系判别的议论文句子排序研究

议论文自动生成是自然语言生成中一项极具挑战性的任务,与诗歌、故事等生成任务不同,所生成的文章需要句子语义明确、论证结构清晰并合理地表达出核心论点。上述特点使得现有的预训练模型难以准确地建模并自动生成,因此传统的检索式方法成为解决该问题的主要方式。但前人方法在句子检索和排序过程中只考虑了语义相关度,忽视了对逻辑论证关系的判别,导致语义不连贯、论证逻辑倒置等问题。针对上述问题,该文将自然语言推理应用于论证关系逻辑判别任务,提出了基于显式语义结构的论证关系逻辑判别方法,新模型在论证判别数据集上取得优于以往自然语言推理模型的效果。同时将论文判别结果作为显式特征应用于议论文句子排序模型,在议论文生成数据集中有效改善了排序模型的逻辑不一致问题并进一步提升了议论文生成系统的总体性能。

Research on Sentence Ordering for Essay Generation via Argumentation Identification

Essay generation is a challenging task in natural language generation. Unlike poetry and story generation, the essay generation demands accurate sentence-level semantics, clear argumentation structure and reasonable expression of core arguments. The state-of-art solution in this task,retrieval based method ignores the identification of logical argumentation relations, resulting in semantic incoherence and inversion of argumentation logic. In this paper, we apply proposes an argumentation identification method based on explicit semantic structure information, achieving better results than previous natural language inference models on the argumentation identification dataset. At the same time, the argumentation identification result is used as an explicit feature to apply to the sentence ordering model for essay generation, which effectively alleviates the logical inconsistency of the ordering model in the essay generation dataset and further improves the overall performance of the essay generation system.