  Generating accurate descriptions for online fashion items is important not
only for enhancing customers' shopping experiences, but also for the increase
of online sales. Besides the need of correctly presenting the attributes of
items, the expressions in an enchanting style could better attract customer
interests. The goal of this work is to develop a novel learning framework for
accurate and expressive fashion captioning. Different from popular work on
image captioning, it is hard to identify and describe the rich attributes of
fashion items. We seed the description of an item by first identifying its
attributes, and introduce attribute-level semantic (ALS) reward and
sentence-level semantic (SLS) reward as metrics to improve the quality of text
descriptions. We further integrate the training of our model with maximum
likelihood estimation (MLE), attribute embedding, and Reinforcement Learning
(RL). To facilitate the learning, we build a new FAshion CAptioning Dataset
(FACAD), which contains 993K images and 130K corresponding enchanting and
diverse descriptions. Experiments on FACAD demonstrate the effectiveness of our
model.
