Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction
This addresses a specific problem in e-commerce product data extraction, offering incremental improvements for rare and ambiguous attributes.
The paper tackles the challenge of handling rare and ambiguous queries in attribute value extraction from e-commerce data by proposing a knowledge-driven query expansion method, which improves macro F1 by 6.08 overall and up to 7.82 for rare attributes.
A key challenge in attribute value extraction (AVE) from e-commerce sites is how to handle a large number of attributes for diverse products. Although this challenge is partially addressed by a question answering (QA) approach which finds a value in product data for a given query (attribute), it does not work effectively for rare and ambiguous queries. We thus propose simple knowledge-driven query expansion based on possible answers (values) of a query (attribute) for QA-based AVE. We retrieve values of a query (attribute) from the training data to expand the query. We train a model with two tricks, knowledge dropout and knowledge token mixing, which mimic the imperfection of the value knowledge in testing. Experimental results on our cleaned version of AliExpress dataset show that our method improves the performance of AVE (+6.08 macro F1), especially for rare and ambiguous attributes (+7.82 and +6.86 macro F1, respectively).