Learning to Control the Fine-grained Sentiment for Story Ending Generation

Fuli Luo, Damai Dai, Pengcheng Yang, Tianyu Liu, Baobao Chang, Zhifang Sui, Xu Sun


Abstract
Automatic story ending generation is an interesting and challenging task in natural language generation. Previous studies are mainly limited to generate coherent, reasonable and diversified story endings, and few works focus on controlling the sentiment of story endings. This paper focuses on generating a story ending which meets the given fine-grained sentiment intensity. There are two major challenges to this task. First is the lack of story corpus which has fine-grained sentiment labels. Second is the difficulty of explicitly controlling sentiment intensity when generating endings. Therefore, we propose a generic and novel framework which consists of a sentiment analyzer and a sentimental generator, respectively addressing the two challenges. The sentiment analyzer adopts a series of methods to acquire sentiment intensities of the story dataset. The sentimental generator introduces the sentiment intensity into decoder via a Gaussian Kernel Layer to control the sentiment of the output. To the best of our knowledge, this is the first endeavor to control the fine-grained sentiment for story ending generation without manually annotating sentiment labels. Experiments show that our proposed framework can generate story endings which are not only more coherent and fluent but also able to meet the given sentiment intensity better.
Anthology ID:
P19-1603
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6020–6026
Language:
URL:
https://aclanthology.org/P19-1603
DOI:
10.18653/v1/P19-1603
Bibkey:
Cite (ACL):
Fuli Luo, Damai Dai, Pengcheng Yang, Tianyu Liu, Baobao Chang, Zhifang Sui, and Xu Sun. 2019. Learning to Control the Fine-grained Sentiment for Story Ending Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6020–6026, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Learning to Control the Fine-grained Sentiment for Story Ending Generation (Luo et al., ACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/P19-1603.pdf
Data
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