  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).
