  Given an image and a reference caption, the image caption editing task aims
to correct the misalignment errors and generate a refined caption. However, all
existing caption editing works are implicit models, ie, they directly produce
the refined captions without explicit connections to the reference captions. In
this paper, we introduce a new task: Explicit Caption Editing (ECE). ECE models
explicitly generate a sequence of edit operations, and this edit operation
sequence can translate the reference caption into a refined one. Compared to
the implicit editing, ECE has multiple advantages: 1) Explainable: it can trace
the whole editing path. 2) Editing Efficient: it only needs to modify a few
words. 3) Human-like: it resembles the way that humans perform caption editing,
and tries to keep original sentence structures. To solve this new task, we
propose the first ECE model: TIger. TIger is a non-autoregressive
transformer-based model, consisting of three modules: Tagger_del, Tagger_add,
and Inserter. Specifically, Tagger_del decides whether each word should be
preserved or not, Tagger_add decides where to add new words, and Inserter
predicts the specific word for adding. To further facilitate ECE research, we
propose two new ECE benchmarks by re-organizing two existing datasets, dubbed
COCO-EE and Flickr30K-EE, respectively. Extensive ablations on both two
benchmarks have demonstrated the effectiveness of TIger.
