- E. Xing. 2020. 15:Case Study of Deep Generative Models: Text Generation. Abstract: The goal of text generation is to generate natural language from input data or machine representations. It spans a broad set of natural language processing (NLP) tasks. These tasks includes dialog system, machine translation, summarization, description generation, captioning and speech recognition. There are basically two center goals in all these tasks. The first center goal is generating human-like, grammatical, and readable text, or so called natural language. The second goal is generating text that contains desired information inferred from inputs. For example, in machine translation the generated target sentence should has the same meaning with the source input sentence. In data description tasks, the generated report should describe the input data table. And for other tasks like attribute control the generated sentence should also contain the same kind of information as input like generating ”I like this restaurant” for a positive sentiment input. Same goals is also in conversation control that we should control conversation strategy and topic to the specified input.
