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Multimodal conditionality in transformer-based natural language models hasdemonstrated state-of-the-art performance in the task of product descriptiongeneration. Recent approaches condition a language model on one or more imagesand other textual metadata to achieve near-human performance for describingproducts from e-commerce stores. However, generated descriptions may exhibitdegrees of inaccuracy or even contradictory claims relative to the inputs of agiven product. In this paper, we propose a controllable language generationframework called Extract-Finetune-Boost (XFBoost), which addresses the problemof inaccurate low-quality inference. By using visual semantic attributes asconstraints at the decoding stage of the generation process and finetuning thelanguage model with policy gradient techniques, the XFBoost framework is foundto produce significantly more descriptive text with higher image relevancy,outperforming baselines and lowering the frequency of factually inaccuratedescriptions. We further demonstrate the application of XFBoost to onlinelearning wherein human-in-the-loop critics improve language models with activefeedback.

XFBoost: Improving Text Generation with Controllable Decoders