# Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG

## TL;DR

This paper introduces YelpNLG, a large dataset and method for jointly controlling semantics and style in neural natural language generation, addressing data scarcity and output dullness issues.

## Contribution

It provides a scalable approach to create rich training data from user reviews and demonstrates joint control of semantic and stylistic aspects in neural NLG.

## Key findings

- Models can control lexical choice, length, and sentiment.
- Generated outputs successfully match multiple style targets.
- The methodology is reusable for other domains.

## Abstract

Neural natural language generation (NNLG) from structured meaning representations has become increasingly popular in recent years. While we have seen progress with generating syntactically correct utterances that preserve semantics, various shortcomings of NNLG systems are clear: new tasks require new training data which is not available or straightforward to acquire, and model outputs are simple and may be dull and repetitive. This paper addresses these two critical challenges in NNLG by: (1) scalably (and at no cost) creating training datasets of parallel meaning representations and reference texts with rich style markup by using data from freely available and naturally descriptive user reviews, and (2) systematically exploring how the style markup enables joint control of semantic and stylistic aspects of neural model output. We present YelpNLG, a corpus of 300,000 rich, parallel meaning representations and highly stylistically varied reference texts spanning different restaurant attributes, and describe a novel methodology that can be scalably reused to generate NLG datasets for other domains. The experiments show that the models control important aspects, including lexical choice of adjectives, output length, and sentiment, allowing the models to successfully hit multiple style targets without sacrificing semantics.