Controllable Neural Story Generation via Reinforcement Learning

By Pradyumna Tambwekar, Murtaza Dhuliawala, Animesh Mehta, Lara J. Martin, Brent Harrison, Mark O. Riedl. 2018

13 citations recorded by the source corpus.

Subfields: Natural Language Processing, Reinforcement Learning

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 Processing

1. Geoffrey Irving
2. Jeff Wu
3. Damai Dai
4. Xu Sun
5. Nisan Stiennon
6. Paul Christiano
7. Tianyu Liu
8. Fuli Luo

In Reinforcement Learning

1. Geoffrey Irving
2. Sergey Levine
3. Paul Christiano
4. Dario Amodei
5. Jeff Wu
6. Damai Dai
7. Xu Sun
8. Fuli Luo

Citations in the corpus

• Fine-Tuning Language Models from Human Preferences (2019, background): “, 2017), or event detectors for story generation (Tambwekar et al., 2018).”
• Learning to summarize from human feedback (2020, background): “There is also a rich literature on using RL to optimize automatic metrics for NLP tasks, such as ROUGE for summarization [50, 65, 45, 15, 19], BLEU for translation [50, 66, 1, 43], and other domains [61, 27, 26].”
• Learning to Control the Fine-grained Sentiment for Story Ending Generation (2019, background): “Tambwekar et al. (2018) introduces a policy gradient learning approach to ensure that the model ends with a specific type of event given in advance.”