Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints
Zhenyi Wang, Xiaoyang Wang, Bang An, Dong Yu, Changyou Chen · 2020
Text generation from a knowledge base aims to translate knowledge triples to naturallanguage descriptions.Most existing methods ignore the faithfulness between a generated text description and the original table, leading to generated information that goes beyond the content of the table.In this paper, for the first time, we propose a novel Transformerbased generation framework to achieve the goal.The core techniques in our method to enforce faithfulness include a new table-text optimal-transport matching loss and a tabletext embedding similarity loss based on the Transformer model.Furthermore, to evaluate faithfulness, we propose a new automatic metric specialized to the table-to-text generation problem.We also provide detailed analysis on each component of our model in our experiments.Automatic and human evaluations show that our framework can significantly outperform state-of-the-art by a large margin.