Generating an informative and attractive title for the product is a crucial
task for e-commerce. Most existing works follow the standard multimodal natural
language generation approaches, e.g., image captioning, and employ the large
scale of human-labelled datasets to train desirable models. However, for novel
products, especially in a different domain, there are few existing labelled
data. In this paper, we propose a prompt-based approach, i.e., the Multimodal
Prompt Learning framework, to accurately and efficiently generate titles for
novel products with limited labels. We observe that the core challenges of
novel product title generation are the understanding of novel product
characteristics and the generation of titles in a novel writing style. To this
end, we build a set of multimodal prompts from different modalities to preserve
the corresponding characteristics and writing styles of novel products. As a
result, with extremely limited labels for training, the proposed method can
retrieve the multimodal prompts to generate desirable titles for novel
products. The experiments and analyses are conducted on five novel product
categories under both the in-domain and out-of-domain experimental settings.
The results show that, with only 1% of downstream labelled data for training,
our proposed approach achieves the best few-shot results and even achieves
competitive results with fully-supervised methods trained on 100% of training
data; With the full labelled data for training, our method achieves
state-of-the-art results.

本文提出了一种基于多模态提示学习框架的方法，通过仅使用少量标注数据快速高效地为新颖产品生成标题，并在不同场景中进行实验证明，在仅使用 downstream 标签数据 1% 的情况下，该方法实现了最佳 few-shot 学习效果，甚至与完全监督的方法相比也具有竞争力。