December 8, 2019
Neural Information Processing Systems (NeurIPS)
Modern neural sequence generation models are built to either generate tokens step-by-step from scratch or (iteratively) modify a sequence of tokens bounded by a fixed length. In this work, we develop Levenshtein Transformer, a new partially autoregressive model devised for more flexible and amenable sequence generation. Unlike previous approaches, the basic operations of our model are insertion and deletion. The combination of them facilitates not only generation but also sequence refinement allowing dynamic length changes.
By: Jiatao Gu, Changhan Wang, Jake (Junbo) Zhao
Facebook AI Research
Natural Language Processing & Speech