  Sequence-level learning objective has been widely used in captioning tasks to
achieve the state-of-the-art performance for many models. In this objective,
the model is trained by the reward on the quality of its generated captions
(sequence-level). In this work, we show the limitation of the current
sequence-level learning objective for captioning tasks from both theory and
empirical result. In theory, we show that the current objective is equivalent
to only optimizing the precision side of the caption set generated by the model
and therefore overlooks the recall side. Empirical result shows that the model
trained by this objective tends to get lower score on the recall side. We
propose to add a sequence-level exploration term to the current objective to
boost recall. It guides the model to explore more plausible captions in the
training. In this way, the proposed objective takes both the precision and
recall sides of generated captions into account. Experiments show the
effectiveness of the proposed method on both video and image captioning
datasets.
