Zero-shot data-to-text generation via dual learning

Weiming Liao; Bo Chen; Xiaobing Zhao

doi:10.1117/12.3033228

5 July 2024 Zero-shot data-to-text generation via dual learning

Weiming Liao, Bo Chen, Xiaobing Zhao

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318433 (2024) https://doi.org/10.1117/12.3033228
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Data-to-text task aims to convert structured data into natural language text. However, the lack of large parallel data is a major practical problem for many domains in data-to-text generation. To address these issues, this paper proposes a method based on dual learning. Dual learning simultaneously learns two mutually dual generator and extractor, where the generator is responsible for text generation and the extractor for information extraction. Through dual learning, we can effectively utilize the interrelationships between unaligned data, thereby improving the performance of the generation model. We conduct experiments on an advertising datasets, and compare with traditional generation models. Experimental results demonstrate that the dual learning-based method achieves nearly the same performance as fully supervised approaches for the data-to-text generation task validating its effectiveness and feasibility in data-to-text generation tasks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Weiming Liao, Bo Chen, and Xiaobing Zhao "Zero-shot data-to-text generation via dual learning", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318433 (5 July 2024); https://doi.org/10.1117/12.3033228

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