  Most image captioning models are autoregressive, i.e. they generate each word
by conditioning on previously generated words, which leads to heavy latency
during inference. Recently, non-autoregressive decoding has been proposed in
machine translation to speed up the inference time by generating all words in
parallel. Typically, these models use the word-level cross-entropy loss to
optimize each word independently. However, such a learning process fails to
consider the sentence-level consistency, thus resulting in inferior generation
quality of these non-autoregressive models. In this paper, we propose a
Non-Autoregressive Image Captioning (NAIC) model with a novel training
paradigm: Counterfactuals-critical Multi-Agent Learning (CMAL). CMAL formulates
NAIC as a multi-agent reinforcement learning system where positions in the
target sequence are viewed as agents that learn to cooperatively maximize a
sentence-level reward. Besides, we propose to utilize massive unlabeled images
to boost captioning performance. Extensive experiments on MSCOCO image
captioning benchmark show that our NAIC model achieves a performance comparable
to state-of-the-art autoregressive models, while brings 13.9x decoding speedup.
