Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training

By Zhongyang Li, Xiao Ding, Ting-Wei Liu. 2018

42 citations recorded by the source corpus.

Subfields: Natural Language Generation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Generation

1. Tianyu Liu
2. Minlie Huang
3. Jianfeng Gao
4. Zhifang Sui
5. Baobao Chang
6. Mirella Lapata

Citations in the corpus

• Evaluation of Text Generation: A Survey (2020, background): “The second text is more lexically diverse, which is more desirable in many NLG tasks such as conversational bots (Li et al., 2018), story generation (Rashkin et al.”
• Story Ending Selection by Finding Hints From Pairwise Candidate Endings (2019, background): “phrase-based plot generation [32], [33], story ending generation [10], [34], [35], to entire story generation [36].”
• Learning to Control the Fine-grained Sentiment for Story Ending Generation (2019, methodology): “It first generates a story ending using a general sequence-to-sequence model with attention (Luong et al., 2015), and then mod-ifies the sentiment of the story ending towards the target sentiment intensity via a fine-grained sentiment modification method (Liao et al., 2018).”