Controllable Neural Story Plot Generation via Reward Shaping
| Autor: | Tambwekar, Pradyumna, Dhuliawala, Murtaza, Martin, Lara J., Mehta, Animesh, Harrison, Brent, Riedl, Mark O. |
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| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | In International Joint Conference on Artificial Intelligence (IJCAI), Macau, China, Jul. 2019, pp. 5982-5988 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.24963/ijcai.2019/829 |
| Popis: | Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model towards a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques. Comment: Pradyumna Tambwekar & Murtaza Dhuliawala contributed equally |
| Databáze: | arXiv |
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