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Article type: Research Article
Authors: Lv, Fangxinga; * | Liu, Wenfenga; b | Yang, Yuzhena | Gao, Yalinga | Bao, Longqingb
Affiliations: [a] School of Computer, Heze University, Daxue, Heze, Shandong, China | [b] Shandong Dachi Alfa Electric Co., Ltd, Economic Development Zone, Chengwu, Shandong, China
Correspondence: [*] Corresponding author. Fangxing Lv, School of Computer, Heze University, Daxue, Heze, 274015, Shandong, China. Email: [email protected].
Abstract: The automatic generation of natural language is a complex and essential task in text processing. This study proposes a novel approach to address this fundamental problem by leveraging an improved version of DST_BERT, a model that converts input text into a vector representation. Our key contribution lies in the joint optimization of two models, NLU (Natural Language Under-standing) and NLG (Natural Language Generation), which enables us to obtain variable representations within a hidden space. This integration enhances the capabilities of both NLU and NLG in generating coherent and contextually appropriate language. The NLU and NLG models are seamlessly integrated with the hidden variable space, forming a generative representation model. To assess the effectiveness of our proposed approach, we conducted extensive experiments on the E2E and Weather datasets. The results highlight the state-of-the-art performance achieved by our model in generating natural language.
Keywords: Natural language generation, natural language understanding, text summarization
DOI: 10.3233/JIFS-232981
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
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