A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph

Understand

Generating a vivid, novel, and diverse essay with only several given topic words is a challenging task of natural language generation.

• In previous work, there are two problems left unsolved: neglect of sentiment beneath the text and insufficient utilization of topic-related knowledge.
• Therefore, we propose a novel Sentiment-Controllable topic-to-essay generator with a Topic Knowledge Graph enhanced decoder, named SCTKG, which is based on the conditional variational autoencoder (CVAE) framework.
• We firstly inject the sentiment information into the generator for controlling sentiment for each sentence, which leads to various generated essays.