Product attribute value extraction involves identifying the specific values
associated with various attributes from a product profile. While existing
methods often prioritize the development of effective models to improve
extraction performance, there has been limited emphasis on extraction
efficiency. However, in real-world scenarios, products are typically associated
with multiple attributes, necessitating multiple extractions to obtain all
corresponding values. In this work, we propose an Efficient product Attribute
Value Extraction (EAVE) approach via lightweight sparse-layer interaction.
Specifically, we employ a heavy encoder to separately encode the product
context and attribute. The resulting non-interacting heavy representations of
the context can be cached and reused for all attributes. Additionally, we
introduce a light encoder to jointly encode the context and the attribute,
facilitating lightweight interactions between them. To enrich the interaction
within the lightweight encoder, we design a sparse-layer interaction module to
fuse the non-interacting heavy representation into the lightweight encoder.
Comprehensive evaluation on two benchmarks demonstrate that our method achieves
significant efficiency gains with neutral or marginal loss in performance when
the context is long and number of attributes is large. Our code is available
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通过轻量级的稀疏层互动，我们提出了一种高效的产品属性值提取（EAVE）方法，其中使用重型编码器分别编码产品上下文和属性，以实现高效的提取性能，并引入轻型编码器来实现上下文和属性的轻量级交互，该方法在两个基准测试中进行了综合评估并取得了显著的效率收益，同时性能损失较小或无损。