Researches advanced in data-to-text generation based on neural networks

Shengyu Wang; Yanping Wang

doi:10.1117/12.2641838

10 November 2022 Researches advanced in data-to-text generation based on neural networks

Shengyu Wang, Yanping Wang

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123480W (2022) https://doi.org/10.1117/12.2641838
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

Data-to-text generation has always been a research hotspot in multimodal machine learning. Given the structured data such as a list of characteristics of a person or statistical data from an exam, the data-to-text generation aims to automatically generate smooth, true and accurate text which can describe the input data. Thanks to the rapid development of convolutional neural networks, the text generation models based on deep learning have made great breakthroughs in performance, but still face many challenges which mainly include confusion of the output location of different information, generation of non-existent content, duplication of information and data sparseness. In this paper, based on in-depth literature survey, we introduce the representative algorithms from the aspects of improving text evaluation quality, combining deep learning and knowledge, adapting to various expressions and aligning text and structured data. We also summarize the existing problems and prospect the possible future development direction in the data-to-text generation research field.

Citation Download Citation

Shengyu Wang and Yanping Wang "Researches advanced in data-to-text generation based on neural networks", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123480W (10 November 2022); https://doi.org/10.1117/12.2641838

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