Learning Sentiment Memories for Sentiment Modification without Parallel Data

Yi Zhang, Jingjing Xu, Pengcheng Yang, Xu Sun


Abstract
The task of sentiment modification requires reversing the sentiment of the input and preserving the sentiment-independent content. However, aligned sentences with the same content but different sentiments are usually unavailable. Due to the lack of such parallel data, it is hard to extract sentiment independent content and reverse the sentiment in an unsupervised way. Previous work usually can not reconcile sentiment transformation and content preservation. In this paper, motivated by the fact the non-emotional context (e.g., “staff”) provides strong cues for the occurrence of emotional words (e.g., “friendly”), we propose a novel method that automatically extracts appropriate sentiment information from learned sentiment memories according to the specific context. Experiments show that our method substantially improves the content preservation degree and achieves the state-of-the-art performance.
Anthology ID:
D18-1138
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1103–1108
Language:
URL:
https://aclanthology.org/D18-1138
DOI:
10.18653/v1/D18-1138
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/D18-1138.pdf