A Hybrid Natural Language Generation System Integrating Rules and Deep Learning Algorithms

TL;DR

This paper introduces a hybrid natural language generation system that combines rule-based methods with deep learning to produce more human-like, controllable text, along with a new performance measurement approach.

Contribution

It presents a novel hybrid NLG system integrating rules and deep learning, and introduces HMCU for comprehensive performance evaluation.

Findings

Enhanced text quality with human-like writing styles

Improved controllability of generated content

HMCU provides precise performance measurement

Abstract

This paper proposes an enhanced natural language generation system combining the merits of both rule-based approaches and modern deep learning algorithms, boosting its performance to the extent where the generated textual content is capable of exhibiting agile human-writing styles and the content logic of which is highly controllable. We also come up with a novel approach called HMCU to measure the performance of the natural language processing comprehensively and precisely.