A topic-controllable keywords-to-text generator with knowledge base network

Abstract

With the introduction of more recent deep learning models such as encoder-decoder, text generation frameworks have gained a lot of popularity. In Natural Language Generation (NLG), controlling the information and style of the output produced is a crucial and challenging task. The purpose of this paper is to develop informative and controllable text using social media language by incorporating topic knowledge into a keyword-to-text framework. A novel Topic-Controllable Key-to-Text (TC-K2T) generator that focuses on the issues of ignoring unordered keywords and utilising subject-controlled information from previous research is presented. TC-K2T is built on the framework of conditional language encoders. In order to guide the model to produce an informative and controllable language, the generator first inputs unordered keywords and uses subjects to simulate prior human knowledge. Using an additional probability term, the model increases the likelihood of topic words appearing in the generated text to bias the overall distribution. The proposed TC-K2T can produce more informative and controllable senescence, outperforming state-of-the-art models, according to empirical research on automatic evaluation metrics and human annotations.