2023.emnlp-industry.16@ACL

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#1 Does Named Entity Recognition Truly Not Scale Up to Real-world Product Attribute Extraction? [PDF2] [Copy] [Kimi4] [REL]

Authors: Wei-Te Chen, Keiji Shinzato, Naoki Yoshinaga, Yandi Xia

The key challenge in the attribute-value extraction (AVE) task from e-commerce sites is the scalability to diverse attributes for a large number of products in real-world e-commerce sites. To make AVE scalable to diverse attributes, recent researchers adopted a question-answering (QA)-based approach that additionally inputs the target attribute as a query to extract its values, and confirmed its advantage over a classical approach based on named-entity recognition (NER) on real-word e-commerce datasets. In this study, we argue the scalability of the NER-based approach compared to the QA-based approach, since researchers have compared BERT-based QA-based models to only a weak BiLSTM-based NER baseline trained from scratch in terms of only accuracy on datasets designed to evaluate the QA-based approach. Experimental results using a publicly available real-word dataset revealed that, under a fair setting, BERT-based NER models rival BERT-based QA models in terms of the accuracy, and their inference is faster than the QA model that processes the same product text several times to handle multiple target attributes.

Subject: EMNLP.2023 - Industry Track