Multimodal Prompt Learning for Product Title Generation with Extremely
  Limited Labels
Bang Yang Fenglin Liu Zheng Li Qingyu Yin Chenyu You Bing Yin Yuexian Zou
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.