Controllable neural text generation Researchers have proposed several approaches to control aspects of RNN-based natural language generation such as sentiment, length, speaker style and tense Ficler and Goldberg, 2017;Ghazvininejad et al., 2017;Hu et al., 2017;Kikuchi et al., 2016;Peng et al., 2018;Wang et al., 2017).
several works use control to tackle the same common sequence-to-sequence problems we address here (particularly genericness and unrelated output), in the context of single-turn response generation (Baheti et al., 2018;Li et al., 2016aLi et al., , 2017aShen et al., 2017;Zhang et al., 2018a;.