Event Representations for Automated Story Generation with Deep Neural Nets

Understand

Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story.

• We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora.
• To date, recurrent neural networks that learn language models at character, word, or sentence levels have had little success generating coherent stories.
• We explore the question of event representations that provide a mid-level of abstraction between words and sentences in order to retain the semantic information of the original data while minimizing event sparsity.