Neural Data-to-Text Generation with Dynamic Content Planning
This work addresses issues in data-to-text generation for applications like automated reporting, though it is incremental as it builds on existing neural models.
The paper tackled the problem of neural data-to-text generation models missing vital information and producing inconsistent descriptions by proposing a model with dynamic content planning and a reconstruction mechanism, achieving superior performance on the ROTOWIRE dataset with improvements in relation generation, content selection, content ordering, and BLEU metrics.
Neural data-to-text generation models have achieved significant advancement in recent years. However, these models have two shortcomings: the generated texts tend to miss some vital information, and they often generate descriptions that are not consistent with the structured input data. To alleviate these problems, we propose a Neural data-to-text generation model with Dynamic content Planning, named NDP for abbreviation. The NDP can utilize the previously generated text to dynamically select the appropriate entry from the given structured data. We further design a reconstruction mechanism with a novel objective function that can reconstruct the whole entry of the used data sequentially from the hidden states of the decoder, which aids the accuracy of the generated text. Empirical results show that the NDP achieves superior performance over the state-of-the-art on ROTOWIRE dataset, in terms of relation generation (RG), content selection (CS), content ordering (CO) and BLEU metrics. The human evaluation result shows that the texts generated by the proposed NDP are better than the corresponding ones generated by NCP in most of time. And using the proposed reconstruction mechanism, the fidelity of the generated text can be further improved significantly.