@inproceedings{wang-etal-2017-statistical,
title = "A Statistical Framework for Product Description Generation",
author = "Wang, Jinpeng and
Hou, Yutai and
Liu, Jing and
Cao, Yunbo and
Lin, Chin-Yew",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-2032",
pages = "187--192",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Statistical Framework for Product Description Generation
%A Wang, Jinpeng
%A Hou, Yutai
%A Liu, Jing
%A Cao, Yunbo
%A Lin, Chin-Yew
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F wang-etal-2017-statistical
%X 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.
%U https://aclanthology.org/I17-2032
%P 187-192
Markdown (Informal)
[A Statistical Framework for Product Description Generation](https://aclanthology.org/I17-2032) (Wang et al., IJCNLP 2017)
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.