T2S: An Encoder-Decoder Model for Topic-Based Natural Language Generation

Natural language generation (NLG) plays a critical role in various natural language processing (NLP) applications. And the topics provide a powerful tool to understand the natural language. We propose a novel topic-based NLG model which can generate topic coherent sentences given single topic or combination of topics. The model is an extension of the recurrent encoder-decoder framework by introducing a global topic embedding matrix. Experimental results show that our encoder can not only transform a source sentence to a representative topic distribution which can give a better interpretation of the source sentence, but also generate topic coherent and diversified sentences given different topic distribution without any text-level input.