Zhendong Yang


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A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph
Lin Qiao | Jianhao Yan | Fandong Meng | Zhendong Yang | Jie Zhou
Findings of the Association for Computational Linguistics: EMNLP 2020

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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The CASIA Phrase-Based Machine Translation System
Wei Pang | Zhendong Yang | Zhenbiao Chen | Wei Wei | Bo Xu | Chengqing Zong
Proceedings of the Second International Workshop on Spoken Language Translation