@inproceedings{qiao-etal-2020-sentiment,
title = "A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph",
author = "Qiao, Lin and
Yan, Jianhao and
Meng, Fandong and
Yang, Zhendong and
Zhou, Jie",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.299",
doi = "10.18653/v1/2020.findings-emnlp.299",
pages = "3336--3344",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph
%A Qiao, Lin
%A Yan, Jianhao
%A Meng, Fandong
%A Yang, Zhendong
%A Zhou, Jie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F qiao-etal-2020-sentiment
%X 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.
%R 10.18653/v1/2020.findings-emnlp.299
%U https://aclanthology.org/2020.findings-emnlp.299
%U https://doi.org/10.18653/v1/2020.findings-emnlp.299
%P 3336-3344
Markdown (Informal)
[A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph](https://aclanthology.org/2020.findings-emnlp.299) (Qiao et al., Findings 2020)
ACL