Automatic Generation of Personalized Comment Based on User Profile

Wenhuan Zeng, Abulikemu Abuduweili, Lei Li, Pengcheng Yang


Abstract
Comments on social media are very diverse, in terms of content, style and vocabulary, which make generating comments much more challenging than other existing natural language generation (NLG) tasks. Besides, since different user has different expression habits, it is necessary to take the user’s profile into consideration when generating comments. In this paper, we introduce the task of automatic generation of personalized comment (AGPC) for social media. Based on tens of thousands of users’ real comments and corresponding user profiles on weibo, we propose Personalized Comment Generation Network (PCGN) for AGPC. The model utilizes user feature embedding with a gated memory and attends to user description to model personality of users. In addition, external user representation is taken into consideration during the decoding to enhance the comments generation. Experimental results show that our model can generate natural, human-like and personalized comments.
Anthology ID:
P19-2032
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Fernando Alva-Manchego, Eunsol Choi, Daniel Khashabi
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
229–235
Language:
URL:
https://aclanthology.org/P19-2032
DOI:
10.18653/v1/P19-2032
Bibkey:
Cite (ACL):
Wenhuan Zeng, Abulikemu Abuduweili, Lei Li, and Pengcheng Yang. 2019. Automatic Generation of Personalized Comment Based on User Profile. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 229–235, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Automatic Generation of Personalized Comment Based on User Profile (Zeng et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-2032.pdf
Code
 Walleclipse/AGPC