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

Qifan Wang, Li Yang, Bhargav Kanagal, Sumit Sanghai, D. Sivakumar, Bin Shu, Zac Yu

Abstract:

Attribute value extraction refers to the task of identifying values
of an attribute of interest from product information. It is an impor-
tant 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 generalizabil-
ity. 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 classi-
fier 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.