  The Controllable Image Captioning (CIC) task aims to generate captions
conditioned on designated control signals. Several structure-related control
signals are proposed to control the semantic structure of sentences, such as
sentence length and Part-of-Speech tag sequences. However, due to the fact that
the accuracy-based reward focuses mainly on contents rather than semantic
structures, existing reinforcement training methods are not applicable to
structure-related CIC models. The lack of reinforcement training leads to
exposure bias and the inconsistency between the optimizing function and
evaluation metrics. In this paper, we propose a novel reinforcement training
method for structure-related control signals: Self-Annotated Training (SAT), to
improve both the accuracy and controllability of CIC models. In SAT, a
recursive annotation mechanism (RAM) is designed to force the input control
signal to match the actual output sentence. Moreover, we propose an extra
alignment reward to finetune the CIC model trained after SAT method, which
further enhances the controllability of models. On the MSCOCO benchmark, we
conduct extensive experiments on different structure-related control signals
and on different baseline models, the results of which demonstrate the
effectiveness and generalizability of our methods.
