  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.
