In Natural Language Generation (NLG), End-to-End (E2E) systems trained
through deep learning have recently gained a strong interest. Such deep models
need a large amount of carefully annotated data to reach satisfactory
performance. However, acquiring such datasets for every new NLG application is
a tedious and time-consuming task. In this paper, we propose a semi-supervised
deep learning scheme that can learn from non-annotated data and annotated data
when available. It uses an NLG and a Natural Language Understanding (NLU)
sequence-to-sequence models which are learned jointly to compensate for the
lack of annotation. Experiments on two benchmark datasets show that, with
limited amount of annotated data, the method can achieve very competitive
results while not using any pre-processing or re-scoring tricks. These findings
open the way to the exploitation of non-annotated datasets which is the current
bottleneck for the E2E NLG system development to new applications.

本研究提出了一种半监督深度学习算法，能够在缺乏注释数据的情况下，利用非注释数据进行学习，采用序列到序列模型的 NLG 和 NLU 模型进行联合学习来实现。实验结果表明，在有限的注释数据下，该方法可以不使用任何预处理或重新评分技巧就能够取得非常有竞争力的效果，为不断发展新的 E2E NLG 应用程序开拓了道路。