Controllable Paraphrase Generation With A Syntactic Exemplar
Mingda Chen, Qingming Tang, Sam Wiseman, Kevin Gimpel . Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019 – 105 citations
Prior work on controllable text generation usually assumes that the controlled attribute can take on one of a small set of values known a priori. In this work, we propose a novel task, where the syntax of a generated sentence is controlled rather by a sentential exemplar. To evaluate quantitatively with standard metrics, we create a novel dataset with human annotations. We also develop a variational model with a neural module specifically designed for capturing syntactic knowledge and several multitask training objectives to promote disentangled representation learning. Empirically, the proposed model is observed to achieve improvements over baselines and learn to capture desirable characteristics.