Learning to Infer Product Attribute Values From Descriptive Texts and Images

Abstract

Online marketplaces are able to offer a staggering array of products that no physical store can match. While this makes it more likely for customers to find what they want, in order for online providers to ensure a smooth and efficient user experience, they must maintain well-organized catalogs, which depends greatly on the availability of per-product attribute values such as color, material, brand, to name a few. Unfortunately, such information is often incomplete or even missing in practice, and therefore we have to resort to predictive models as well as other sources of information to impute missing attribute values. In this talk we present the deep learning-based approach that we have developed at Rakuten Group to extract attribute values from product descriptive texts and images. Starting from pretrained architectures to encode textual and visual modalities, we discuss several refinements and improvements that we find necessary to achieve satisfactory performance and meet strict business requirements, namely improving recall while maintaining a high precision (>= 95%). Our methodology is driven by a systematic investigation into several practical research questions surrounding multimodality, which we revisit in this talk. At the heart of our multimodal architecture, is a new method to combine modalities inspired by empirical cross-modality comparisons. We present the latter component in details, point out one of its major limitations, namely exacerbating the issue of modality collapse, i.e., when the model forgets one modality, and describe our mitigation to this problem based on a principled regularization scheme. We present various empirical results on both Rakuten data as well as public benchmark datasets, which provide evidence of the benefits of our approach compared to several strong baselines. We also share some insights to characterise the circumstances in which the proposed model offers the most significant improvements. We conclude this talk by criticising the current model and discussing possible future developments and improvements. Our model is successfully deployed in Rakuten Ichiba - a Rakuten marketplace - and we believe that our investigation into multimodal attribute value extraction for e-commerce will benefit other researchers and practitioners alike embarking on similar journeys.