Cross-Encoder Data Annotation for Bi-Encoder Based Product Matching
Justin Chiu, Keiji Shinzato · 2022
Matching a seller listed item to an appropriate product is an important step for an e-commerce platform.With the recent advancement in deep learning, there are different encoder based approaches being proposed as solution.When textual data for two products are available, cross-encoder approaches encode them jointly while bi-encoder approaches encode them separately.Since cross-encoders are computationally heavy, approaches based on bi-encoders are a common practice for this challenge.In this paper, we propose cross-encoder data annotation; a technique to annotate or refine human annotated training data for bi-encoder models using a cross-encoder model.This technique enables us to build a robust model without annotation on newly collected training data or further improve model performance on annotated training data.We evaluate the cross-encoder data annotation on the product matching task using a real-world e-commerce dataset containing 104 million products.Experimental results show that the cross-encoder data annotation improves 4% absolute accuracy when no annotation for training data is available, and 2% absolute accuracy when annotation for training data is available.