Abstract
We present in this paper a statistical framework that generates accurate and fluent product description from product attributes. Specifically, after extracting templates and learning writing knowledge from attribute-description parallel data, we use the learned knowledge to decide what to say and how to say for product description generation. To evaluate accuracy and fluency for the generated descriptions, in addition to BLEU and Recall, we propose to measure what to say (in terms of attribute coverage) and to measure how to say (by attribute-specified generation) separately. Experimental results show that our framework is effective.- Anthology ID:
- I17-2032
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 187–192
- Language:
- URL:
- https://aclanthology.org/I17-2032
- DOI:
- Cite (ACL):
- Jinpeng Wang, Yutai Hou, Jing Liu, Yunbo Cao, and Chin-Yew Lin. 2017. A Statistical Framework for Product Description Generation. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 187–192, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- A Statistical Framework for Product Description Generation (Wang et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/naacl24-info/I17-2032.pdf