The Surface Realization Shared Tasks of 2018 and 2019 were Natural Language Generation shared tasks with the goal of exploring approaches to surface realization from Universal-Dependency-like trees to surface strings for several languages.In the 2018 shared task there was very little difference in the absolute performance of systems trained with and without additional, synthetically created data, and a new rule prohibiting the use of synthetic data was introduced for the 2019 shared task.Contrary to the findings of the 2018 shared task, we show, in experiments on the English 2018 dataset, that the use of synthetic data can have a substantial positive effect -an improvement of almost 8 BLEU points for a previously state-of-the-art system.We analyse the effects of synthetic data, and we argue that its use should be encouraged rather than prohibited so that future research efforts continue to explore systems that can take advantage of such data.

Neural Natural Language Generation (NLG) systems are well known for their unreliability.To overcome this issue, we propose a data augmentation approach which allows us to restrict the output of a network and guarantee reliability.While this restriction means generation will be less diverse than if randomly sampled, we include experiments that demonstrate the tendency of existing neural generation approaches to produce dull and repetitive text, and we argue that reliability is more important than diversity for this task.The system trained using this approach scored 100% in semantic accuracy on the E2E NLG Challenge dataset, the same as a template system.

In natural language generation (NLG), the task is to generate utterances from a more abstract input, such as structured data.An added challenge is to generate utterances that contain an accurate representation of the input, while reflecting the fluency and variety of human-generated text.In this paper, we report experiments with NLG models that can be used in task oriented dialogue systems.We explore the use of additional input to the model to encourage diversity and control of outputs.While our submission does not rank highly using automated metrics, qualitative investigation of generated utterances suggests the use of additional information in neural network NLG systems to be a promising research direction.

Learning to generate fluent natural language from structured data with neural networks has become an common approach for NLG.This problem can be challenging when the form of the structured data varies between examples.This paper presents a survey of several extensions to sequence-to-sequence models to account for the latent content selection process, particularly variants of copy attention and coverage decoding.We further propose a training method based on diverse ensembling to encourage models to learn distinct sentence templates during training.An empirical evaluation of these techniques shows an increase in the quality of generated text across five automated metrics, as well as human evaluation.