A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph

Lin Qiao, Jianhao Yan, Fandong Meng, Zhendong Yang, Jie Zhou


Abstract
Generating a vivid, novel, and diverse essay with only several given topic words is a promising task of natural language generation. Previous work in this task exists two challenging problems: neglect of sentiment beneath the text and insufficient utilization of topic-related knowledge. Therefore, we propose a novel Sentiment Controllable topic-to- essay generator with a Topic Knowledge Graph enhanced decoder, named SCTKG, which is based on the conditional variational auto-encoder (CVAE) framework. We firstly inject the sentiment information into the generator for controlling sentiment for each sentence, which leads to various generated essays. Then we design a Topic Knowledge Graph enhanced decoder. Unlike existing models that use knowledge entities separately, our model treats knowledge graph as a whole and encodes more structured, connected semantic information in the graph to generate a more relevant essay. Experimental results show that our SCTKG can generate sentiment controllable essays and outperform the state-of-the-art approach in terms of topic relevance, fluency, and diversity on both automatic and human evaluation.
Anthology ID:
2020.findings-emnlp.299
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3336–3344
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.299
DOI:
10.18653/v1/2020.findings-emnlp.299
Bibkey:
Cite (ACL):
Lin Qiao, Jianhao Yan, Fandong Meng, Zhendong Yang, and Jie Zhou. 2020. A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3336–3344, Online. Association for Computational Linguistics.
Cite (Informal):
A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph (Qiao et al., Findings 2020)
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PDF:
https://aclanthology.org/2020.findings-emnlp.299.pdf