Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG
This addresses data scarcity and output quality problems for researchers and practitioners in natural language generation, though it is incremental as it builds on existing NNLG methods.
The paper tackled the challenges of acquiring training data and generating dull outputs in neural natural language generation by creating YelpNLG, a corpus of 300,000 parallel meaning representations and stylistically varied texts from user reviews, and enabling joint control of semantics and style in models, with experiments showing successful control over aspects like lexical choice, length, and sentiment without sacrificing semantics.
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.