ACE-BERT: Adversarial Cross-modal Enhanced BERT for E-commerce Retrieval

• URL: http://arxiv.org/abs/2112.07209v1
• Date: Tue, 14 Dec 2021 07:36:20 GMT
• Title: ACE-BERT: Adversarial Cross-modal Enhanced BERT for E-commerce Retrieval
• Authors: Boxuan Zhang, Chao Wei, Yan Jin and Weiru Zhang
• Abstract summary: We propose a novel Adrial Cross-modal Enhanced BERT (ACE-BERT) for efficient E-commerce retrieval. With the pre-trained enhanced BERT as the backbone network, ACE-BERT adopts adversarial learning to ensure the distribution consistency of different modality representations. Experimental results demonstrate that ACE-BERT outperforms the state-of-the-art approaches on the retrieval task.
• Score: 6.274310862007448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays on E-commerce platforms, products are presented to the customers with multiple modalities. These multiple modalities are significant for a retrieval system while providing attracted products for customers. Therefore, how to take into account those multiple modalities simultaneously to boost the retrieval performance is crucial. This problem is a huge challenge to us due to the following reasons: (1) the way of extracting patch features with the pre-trained image model (e.g., CNN-based model) has much inductive bias. It is difficult to capture the efficient information from the product image in E-commerce. (2) The heterogeneity of multimodal data makes it challenging to construct the representations of query text and product including title and image in a common subspace. We propose a novel Adversarial Cross-modal Enhanced BERT (ACE-BERT) for efficient E-commerce retrieval. In detail, ACE-BERT leverages the patch features and pixel features as image representation. Thus the Transformer architecture can be applied directly to the raw image sequences. With the pre-trained enhanced BERT as the backbone network, ACE-BERT further adopts adversarial learning by adding a domain classifier to ensure the distribution consistency of different modality representations for the purpose of narrowing down the representation gap between query and product. Experimental results demonstrate that ACE-BERT outperforms the state-of-the-art approaches on the retrieval task. It is remarkable that ACE-BERT has already been deployed in our E-commerce's search engine, leading to 1.46% increase in revenue.

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