Abstract
Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order. In this work, we present a neural network architecture which incorporates content selection and planning without sacrificing end-to-end training. We decompose the generation task into two stages. Given a corpus of data records (paired with descriptive documents), we first generate a content plan highlighting which information should be mentioned and in which order and then generate the document while taking the content plan into account. Automatic and human-based evaluation experiments show that our model outperforms strong baselines improving the state-of-the-art on the recently released RotoWire dataset.
Abstract (translated)
数据到文本生成的最新进展已经导致使用大规模数据集和神经网络模型，这些模型是端到端训练的，没有明确地模拟说什么和以什么顺序。在这项工作中，我们提出了一个神经网络架构，其中包含内容选择和规划，而不会牺牲端到端的培训。我们将生成任务分解为两个阶段。给定一组数据记录（与描述性文档配对），我们首先生成一个内容计划，突出显示应该提及哪些信息以及以何种顺序，然后在考虑内容计划的同时生成文档。自动和基于人工的评估实验表明，我们的模型优于强基线，改善了最近发布的RotoWire数据集的最新技术水平。
URL
https://arxiv.org/abs/1809.00582
