A Hybrid Natural Language Generation System Integrating Rules and Deep Learning Algorithms
This work addresses the need for more agile and controllable text generation in natural language processing, though it appears incremental as it combines existing methods.
The paper tackled the problem of generating human-like and controllable text by proposing a hybrid natural language generation system that integrates rule-based approaches and deep learning algorithms, resulting in improved performance as measured by a novel HMCU evaluation method.
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.