Previous studies have highlighted the advantages of pipeline neural architectures over end-to-end models, particularly in reducing text hallucination. In this study, we extend prior research by integrating pretrained language models (PLMs) into a pipeline framework, using both fine-tuning and prompting methods. Our findings show that fine-tuned PLMs consistently generate high quality text, especially within end-to-end architectures and at intermediate stages of the pipeline across various domains. These models also outperform prompt-based ones on automatic evaluation metrics but lag in human evaluations. Compared to the standard five-stage pipeline architecture, a streamlined three-stage pipeline, which only include ordering, structuring, and surface realization, achieves superior performance in fluency and semantic adequacy according to the human evaluation.

In this paper, we present our approach to the GEM Shared Task at the INLG’24 Generation Challenges, which focuses on generating data-to-text in multiple languages, including low-resource languages, from WebNLG triples. We employ a combination of end-to-end and pipeline neural architectures for English text generation. To extend our methodology to Hindi, Korean, Arabic, and Swahili, we leverage a neural machine translation model. Our results demonstrate that our approach achieves competitive performance in the given task.