Automated learning of templates for data to text generation: comparing rule based, statistical and neural methods
The current study investigated novel techniques and methods for trainable approaches to data to text generation. Neural Machine Translation was explored for the conversion from data to text as well as the addition of extra templatization steps of the data input and text output in the conversion process. Evaluation using BLEU did not find the Neural Machine Translation technique to perform any better compared to rule based or Statistical Machine Translation, and the templatization method seemed to perform similarly or sometimes worse compared to direct data to text conversion. However, the human evaluation metrics indicated that Neural Machine Translation yielded the highest quality output and that the templatization method was able to increase text quality in multiple situations.
INLG