Zheng Wenfeng, Yin Lirong
School of Automation, University of Electronic Science and Technology of China, Chengdu, China.
Department of Geography and Anthropology, Louisiana State University and Agricultural and Mechanical College, Baton Rouge, Louisiana, United States.
PeerJ Comput Sci. 2022 Apr 12;8:e908. doi: 10.7717/peerj-cs.908. eCollection 2022.
The whole sentence representation reasoning process simultaneously comprises a sentence representation module and a semantic reasoning module. This paper combines the multi-layer semantic representation network with the deep fusion matching network to solve the limitations of only considering a sentence representation module or a reasoning model. It proposes a joint optimization method based on multi-layer semantics called the Semantic Fusion Deep Matching Network (SCF-DMN) to explore the influence of sentence representation and reasoning models on reasoning performance. Experiments on text entailment recognition tasks show that the joint optimization representation reasoning method performs better than the existing methods. The sentence representation optimization module and the improved optimization reasoning model can promote reasoning performance when used individually. However, the optimization of the reasoning model has a more significant impact on the final reasoning results. Furthermore, after comparing each module's performance, there is a mutual constraint between the sentence representation module and the reasoning model. This condition restricts overall performance, resulting in no linear superposition of reasoning performance. Overall, by comparing the proposed methods with other existed methods that are tested using the same database, the proposed method solves the lack of in-depth interactive information and interpretability in the model design which would be inspirational for future improving and studying of natural language reasoning.
整个句子表示推理过程同时包括一个句子表示模块和一个语义推理模块。本文将多层语义表示网络与深度融合匹配网络相结合，以解决仅考虑句子表示模块或推理模型的局限性。它提出了一种基于多层语义的联合优化方法，称为语义融合深度匹配网络（SCF-DMN），以探究句子表示和推理模型对推理性能的影响。文本蕴含识别任务的实验表明，联合优化表示推理方法比现有方法表现更好。句子表示优化模块和改进的优化推理模型单独使用时都可以提升推理性能。然而，推理模型的优化对最终推理结果有更显著的影响。此外，在比较每个模块的性能后，句子表示模块和推理模型之间存在相互约束。这种情况限制了整体性能，导致推理性能没有线性叠加。总体而言，通过将所提出的方法与使用相同数据库测试的其他现有方法进行比较，所提出的方法解决了模型设计中缺乏深度交互信息和可解释性的问题，这将为未来自然语言推理的改进和研究提供启发。
