Abstract
Extracting product attribute value information is vital for many e-commerce applications. One of the most crucial product attributes is the brand, as it significantly impacts customers’ purchasing decisions and behaviour. Consequently, it is critical for e-commerce platforms to automatically and accurately identify brand values from product descriptions. Most existing methods focus on brand value extraction from text descriptions using sequence tagging and question answering techniques. However, brand values are often not mentioned explicitly in the product descriptions. Also, these approaches are designed without paying attention to product categories, which are important for brand value identification. In this work, we propose a novel category-aware generative approach for brand value identification (GAVI). In particular, we formulate the brand value identification problem as a sequence-to-sequence generation task. We use the T5 language model as the backbone of our approach. This allows us to identify brand values that are not explicitly mentioned in the title in a generative manner. We then propose to highlight the product categories inside our model input, making the approach category-aware. We conduct extensive experiments on a public dataset for brand value identification. The experimental results demonstrate that our generation-based approach outperforms existing extraction-based methods. Our code is released along with the fine-tuned models presented in the paper, which are also available as a demo.