  Novel Object Captioning is a zero-shot Image Captioning task requiring
describing objects not seen in the training captions, but for which information
is available from external object detectors. The key challenge is to select and
describe all salient detected novel objects in the input images. In this paper,
we focus on this challenge and propose the ECOL-R model (Encouraging Copying of
Object Labels with Reinforced Learning), a copy-augmented transformer model
that is encouraged to accurately describe the novel object labels. This is
achieved via a specialised reward function in the SCST reinforcement learning
framework (Rennie et al., 2017) that encourages novel object mentions while
maintaining the caption quality. We further restrict the SCST training to the
images where detected objects are mentioned in reference captions to train the
ECOL-R model. We additionally improve our copy mechanism via Abstract Labels,
which transfer knowledge from known to novel object types, and a Morphological
Selector, which determines the appropriate inflected forms of novel object
labels. The resulting model sets new state-of-the-art on the nocaps (Agrawal et
al., 2019) and held-out COCO (Hendricks et al., 2016) benchmarks.
