# Designing a Symbolic Intermediate Representation for Neural Surface Realization

## TL;DR

This paper introduces a symbolic intermediate representation for neural surface realization in natural language generation, improving output quality and enabling integration with non-neural content planning.

## Contribution

It proposes a novel symbolic intermediate representation that enhances neural surface realization and outperforms existing systems on the E2E dataset.

## Key findings

- High-quality surface realization from the symbolic representation
- Outperforms the E2E challenge winner on the dataset
- Framework supports semi-supervised pretraining for neural models

## Abstract

Generated output from neural NLG systems often contain errors such as hallucination, repetition or contradiction. This work focuses on designing a symbolic intermediate representation to be used in multi-stage neural generation with the intention of reducing the frequency of failed outputs. We show that surface realization from this intermediate representation is of high quality and when the full system is applied to the E2E dataset it outperforms the winner of the E2E challenge. Furthermore, by breaking out the surface realization step from typically end-to-end neural systems, we also provide a framework for non-neural content selection and planning systems to potentially take advantage of semi-supervised pretraining of neural surface realization models.