Recent neural models for data-to-text generation are mostly based on data-driven end-toend training over encoder-decoder networks. Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data. This is a critical issue especially in domains that require inference or calculations over raw data. In this paper, we attempt to improve the fidelity of neural data-to-text generation by utilizing pre-executed symbolic operations. We propose a framework called Operationguided Attention-based sequence-to-sequence network (OpAtt), with a specifically designed gating mechanism as well as a quantization module for operation results to utilize information from pre-executed operations. Experiments on two sports datasets show our proposed method clearly improves the fidelity of the generated texts to the input structured data.
REF attempted to improve the fidelity of neural table-to-text generation by utilizing pre-executed symbolic operations in a sequence-to-sequence model.
Operations Guided Neural Networks for High Fidelity Data-To-Text Generation
ArXiv
ArXiv
Computer Science