Collection of Papers related to Computational Storytelling WIP
- (AIIDE) The Artificial Intelligence for Interactive Digital Entertainment Conference
- (ICIDS) International Conference on Interactiate Digital Storytelling
- (NUSE-ACL Workshop) Workshop on Narrative Understanding, Storylines, and Events
- (IEEE COG) IEEE Conference on Games
Planning Algorithms (Separate File) Symbolic Story Generation
Explicit outlines can be in the form of Structured Format (ex. SRL Tags - Predicate,Argument Form) Sentence Keywords (Event, Emotion, Sentiment,...)
- Fan, Angela et al. “Strategies for Structuring Story Generation.” ACL (2019).
- Prompt -> SRL Role Plan
- Wang, L. et al. “Plan-CVAE: A Planning-Based Conditional Variational Autoencoder for Story Generation.” CNCL (2020).
- RAKE Keywords as outline
- Goldfarb-Tarrant, Seraphina et al. “Content Planning for Neural Story Generation with Aristotelian Rescoring.” EMNLP (2020).
- SRL tag plot -> Rescorer for better plot
- Xu, Peng et al. “MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models.” EMNLP (2020).
- Keywords as outline, Knowledge Retrieval using keywords
- Brahman, Faeze and S. Chaturvedi. “Modeling Protagonist Emotions for Emotion-Aware Storytelling.” EMNLP (2020).
- Generate text that adheres to title & emotion arc
- Doesn't generate emotion arc
- Ammanabrolu, Prithviraj et al. “Automated Storytelling via Causal, Commonsense Plot Ordering.” ArXiv abs/2009.00829 (2020): n. pag.
- Plot infilling between major plot points (plot points as context)
- Forward, backward plot graph branching using COMET
- Lin, Shih-ting et al. “Conditional Generation of Temporally-ordered Event Sequences.” ArXiv abs/2012.15786 (2020): n. pag.
- Event infilling given event sequence (Event Deletion)
- Peng, Nanyun et al. “Towards Controllable Story Generation.” (NAACL WS 2018).
- Ending Valence Control
- Fan, Angela et al. “Hierarchical Neural Story Generation.” ACL (2018).
- Yao, Lili et al. “Plan-And-Write: Towards Better Automatic Storytelling.” AAAI (2019).
- Ippolito, Daphne et al. “Toward Better Storylines with Sentence-Level Language Models.” ACL (2020).
- Predict sentence embedding for continuation & select from candidates
- Polceanu, Mihai et al. “Narrative Plan Generation with Self-Supervised Learning.” AAAI 2020 (2020).
- Forward search PDDL domains to generate target plans
- Generate novel plan by decoding noise given as latent vector using Regularzed AE
- Peng, Nanyun et al. “Towards Controllable Story Generation.” (NAACL WS 2018).
- Storyline Control (keyword -> story)
- Fan, Angela et al. “Strategies for Structuring Story Generation.” ACL (2019).
- Entity modelling (Placeholder prediction)
- Coreference-based entity reference generation: different string given context & previous prediction for same placeholder
- Tu, Lifu et al. “Generating Diverse Story Continuations with Controllable Semantics.” NGT@EMNLP-IJCNLP (2019)
- Generate story text given controllable value
- Xu, Peng et al. “MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models.” EMNLP (2020).
- Experiement controllability of story text with antonyms
Evaluate generated story perplexity? diversity?
- Unsupervised learning of narrative event chains
- Movie Script Summarization as Graph-based Scene Extraction
- CaTeRS
- Automatic Identification of Narrative Diegesis and Point of View
- StoryFramer
- Story Understanding with External Knowledge Based Attention
- Automatic Extraction of Narrative Structure from Long Form Text
- Learning to Predict Explainable Plots for Neural Story Generation
- Movie Plot Analysis via Plot Identification
- Modelling Suspense in Short Stories as Uncertainty Reduction over Neural Representation
- Automatic Extraction of Personal Events from Dialogue
- GLUCOSE
- Multi-view Story Characterization from Movie Plot Synopses and Reviews
- Story Quality as a Matter of Perception
- UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation
- Character-based kernels for novelistic plot structure
- Character-to-Character Sentiment Analysis in Shakespeare’s Plays
- Character-to-Character Sentiment Analysis in Shakespeare’s Plays Film Characters
- A Bayesian Mixed Effects Model of Literary Character
Narrative Diegesis, Focalization, ...