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Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach

Qifan Wang
D. Sivakumar
Bin Shu
Jon Elsas
SIGKDD 2020 (2020)


Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. It is an important research topic which has been widely studied in e-Commerce and relation learning. There are two main limitations in existing attribute value extraction methods: scalability and generalizability. Most existing methods treat each attribute independently and build separate models for each of them, which are not suitable for large scale attribute systems in real-world applications. Moreover, very limited research has focused on generalizing extraction to new attributes. In this work, we propose a novel approach for Attribute Value Extraction via Question Answering (AVEQA) using a multi-task framework. In particular, we build a question answering model which treats each attribute as a question and identifies the answer span corresponding to the attribute value in the product context. A unique BERT contextual encoder is adopted and shared across all attributes to encode both the context and the question, which makes the model scalable. A distilled masked language model with knowledge distillation loss is introduced to improve the model generalization ability. In addition, we employ a no-answer classifier to explicitly handle the cases where there are no values for a given attribute in the product context. The question answering, distilled masked language model and the no answer classification are then combined into a unified multi-task framework. We conduct extensive experiments on a public dataset. The results demonstrate that the proposed approach outperforms several state-of-the-art methods with large margin.