EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913385095888896 |
|---|---|
| author | Yang, Li Wang, Qifan Chi, Jianfeng Liu, Jiahao Wang, Jingang Feng, Fuli Xu, Zenglin Fang, Yi Huang, Lifu Liu, Dongfang |
| author_facet | Yang, Li Wang, Qifan Chi, Jianfeng Liu, Jiahao Wang, Jingang Feng, Fuli Xu, Zenglin Fang, Yi Huang, Lifu Liu, Dongfang |
| contents | 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 \href{https://anonymous.4open.science/r/EAVE-EA18}{here}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06839 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction Yang, Li Wang, Qifan Chi, Jianfeng Liu, Jiahao Wang, Jingang Feng, Fuli Xu, Zenglin Fang, Yi Huang, Lifu Liu, Dongfang Computation and Language 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 \href{https://anonymous.4open.science/r/EAVE-EA18}{here}. |
| title | EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.06839 |