  Multimodal conditionality in transformer-based natural language models has
demonstrated state-of-the-art performance in the task of product description
generation. Recent approaches condition a language model on one or more images
and other textual metadata to achieve near-human performance for describing
products from e-commerce stores. However, generated descriptions may exhibit
degrees of inaccuracy or even contradictory claims relative to the inputs of a
given product. In this paper, we propose a controllable language generation
framework called Extract-Finetune-Boost (XFBoost), which addresses the problem
of inaccurate low-quality inference. By using visual semantic attributes as
constraints at the decoding stage of the generation process and finetuning the
language model with policy gradient techniques, the XFBoost framework is found
to produce significantly more descriptive text with higher image relevancy,
outperforming baselines and lowering the frequency of factually inaccurate
descriptions. We further demonstrate the application of XFBoost to online
learning wherein human-in-the-loop critics improve language models with active
feedback.
