In the e-commerce domain, the accurate extraction of attribute-value pairs
from product listings (e.g., Brand: Apple) is crucial for enhancing search and
recommendation systems. The automation of this extraction process is
challenging due to the vast diversity of product categories and their
respective attributes, compounded by the lack of extensive, accurately
annotated training datasets and the demand for low latency to meet the
real-time needs of e-commerce platforms. To address these challenges, we
introduce GenToC, a novel two-stage model for extracting attribute-value pairs
from product titles. GenToC is designed to train with partially-labeled data,
leveraging incomplete attribute-value pairs and obviating the need for a fully
annotated dataset. Moreover, we introduce a bootstrapping method that enables
GenToC to progressively refine and expand its training dataset. This
enhancement substantially improves the quality of data available for training
other neural network models that are typically faster but are inherently less
capable than GenToC in terms of their capacity to handle partially-labeled
data. By supplying an enriched dataset for training, GenToC significantly
advances the performance of these alternative models, making them more suitable
for real-time deployment. Our results highlight the unique capability of GenToC
to learn from a limited set of labeled data and to contribute to the training
of more efficient models, marking a significant leap forward in the automated
extraction of attribute-value pairs from product titles. GenToC has been
successfully integrated into India's largest B2B e-commerce platform,
IndiaMART.com, achieving a significant increase of 21.1% in recall over the
existing deployed system while maintaining a high precision of 89.5% in this
challenging task.

为了提高搜索和推荐系统，在电子商务领域中准确提取产品列表中的属性 - 值对（例如：品牌：Apple）至关重要。为了应对这些挑战，我们介绍了 GenToC，这是一种从产品标题中提取属性 - 值对的新型两阶段模型，能够以部分标记数据进行训练，并引入了一种引导方法，可以逐步改进和扩展其训练数据集。GenToC 的结果表明，它在从产品标题中自动提取属性 - 值对方面具有独特的能力，为训练更高效的模型做出了重要贡献。