  Text editing, such as grammatical error correction, arises naturally from
imperfect textual data. Recent works frame text editing as a multi-round
sequence tagging task, where operations -- such as insertion and substitution
-- are represented as a sequence of tags. While achieving good results, this
encoding is limited in flexibility as all actions are bound to token-level
tags. In this work, we reformulate text editing as an imitation game using
behavioral cloning. Specifically, we convert conventional sequence-to-sequence
data into state-to-action demonstrations, where the action space can be as
flexible as needed. Instead of generating the actions one at a time, we
introduce a dual decoders structure to parallel the decoding while retaining
the dependencies between action tokens, coupled with trajectory augmentation to
alleviate the distribution shift that imitation learning often suffers. In
experiments on a suite of Arithmetic Equation benchmarks, our model
consistently outperforms the autoregressive baselines in terms of performance,
efficiency, and robustness. We hope our findings will shed light on future
studies in reinforcement learning applying sequence-level action generation to
natural language processing.
