Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints

• URL: http://arxiv.org/abs/2005.00969v1
• Date: Sun, 3 May 2020 02:54:26 GMT
• Title: Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints
• Authors: Zhenyi Wang, Xiaoyang Wang, Bang An, Dong Yu, Changyou Chen
• Abstract summary: We propose a novel Transformer-based generation framework to achieve the goal. Core techniques in our method to enforce faithfulness include a new table-text optimal-transport matching loss. To evaluate faithfulness, we propose a new automatic metric specialized to the table-to-text generation problem.
• Score: 63.84063384518667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text generation from a knowledge base aims to translate knowledge triples to natural language 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 Transformer-based 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 table-text 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.

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