Recent approaches to data-to-text generation have shown great promise thanks
to the use of large-scale datasets and the application of neural network
architectures which are trained end-to-end. These models rely on representation
learning to select content appropriately, structure it coherently, and
verbalize it grammatically, treating entities as nothing more than vocabulary
tokens. In this work we propose an entity-centric neural architecture for
data-to-text generation. Our model creates entity-specific representations
which are dynamically updated. Text is generated conditioned on the data input
and entity memory representations using hierarchical attention at each time
step. We present experiments on the RotoWire benchmark and a (five times
larger) new dataset on the baseball domain which we create. Our results show
that the proposed model outperforms competitive baselines in automatic and
human evaluation.

该论文介绍了一种以实体为中心的神经体系结构，用于生成数据到文本的任务，并通过在 RotoWire 基准和棒球领域的新数据集上进行的实验，表明该模型在自动和人类评估上优于竞争基线。