ADAPT at SR ’20: How Preprocessing and Data Augmentation Help to Improve Surface Realization (2020)

Abstract: In this paper, we describe the ADAPT submission to the Surface Realization Shared Task 2020. We present a neural-based system trained on the English Web Treebank and an augmented dataset, automatically created from existing text corpora.

## introduction surface realization is the final step of an nlg system @xcite . the prior steps provide guidance on the content and structure of a sentence that is to be generated. the goal of this shared task is to generate sentences from structured data with high accuracy @xcite . once this goal has been achieved, we believe neural-based surface realization systems could be incorporated into real-world nlg systems, such as task-oriented dialogue systems @xcite and personalised marketing systems 1 ## system description we made a submission to the surface realization shared task 2020, for the english language dataset: universal dependencies english web treebank @xcite . we use a neural-based system; a sequence-to-sequence model trained on linearized trees. we submitted test outputs to both the open and closed tracks. for the open track we trained the same system on a large augmented dataset. ## results in this section we report our results on the shared task. an explanation of the evaluation methodology, as well as a comparison with other participants, can be found in the shared task description paper @xcite .