Reading List

Following is the my personally interested reading list for individual topics.  

Note: this material on this website is tentative and updated regularly.

Introduction and Language Modeling

• Tomas Mikolov, Martin Karafiát, Lukás Burget, Jan Cernocký, Sanjeev Khudanpur, 2010. Recurrent neural network based language model. INTERSPEECH. 

• Tomas Mikolov, Stefan Kombrink, Lukás Burget, Jan Cernocký, Sanjeev Khudanpur, 2011: Extensions of recurrent neural network language model. ICASSP.

• Rafal Józefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, Yonghui Wu, 5: Exploring the Limits of Language Modeling. CoRR abs/1602.02410.

Event-centric text understanding (Scripts, plot-units, etc.)

• Lehnert, 1981. Plot Units and Narrative Summarization. Cognitive Science.

• Goyal, Riloff, Daume III, 2010. Automatically Producing Plot Unit Representations for Narrative Text. EMNLP. 

• Mooney and DeJong, 1985. Learning schemata for natural language processing. IJCAI.

• Chambers and Jurafsky, 2008. Unsupervised Learning of Narrative Event Chains. ACL.

• Chambers and Jurafsky, 2009. Unsupervised Learning of Narrative Schemas and their Participants. ACL.

• Regneri, Koller, and Pinkal, 2010. Learning script knowledge with web experiments. ACL.

• Jans, Bethard, Vuli´c, and Moens, 2012. Skip n-grams and ranking functions for predicting script events. EACL .

• Chambers, 2013. Event Schema Induction with a Probabilistic Entity-Driven Model. EMNLP. 

• Cheung, Poon, and Vanderwende. 2013. Probabilistic frame induction. NAACL-HLT.

• Modi, and Titov, 2014. Inducing neural models of script knowledge. CoNLL.

• Rudinger, Rastogi, Ferraro, and Van Durme, 2015. Script induction as language modeling. EMNLP (short).

• Pichotta, and Mooney, 2014. Statistical script learning with multi-argument events. EACL .

• Rudinger, Rastogi, Ferraro, and Van Durme, 2015. Script induction as language modeling. EMNLP .

• Pichotta and Mooney, 2016. Learning Statistical Scripts with LSTM Recurrent Neural Networks. AAAI.

• Peng and Roth, 2016. Two Discourse Driven Language Models for Semantics. ACL. 

Character-centric Narrative understanding

• Bamman, O'Connor and Smith, 2013. Learning Latent Personas of Film Characters. ACL.

• Bamman, Underwood and Smith, 2014. A Bayesian Mixed Effects Model of Literary Character. ACL.

• Flekova and Gurevych, 2015. Personality profiling of fictional characters using sense-level links between lexical resources. EMNLP.

• Krishnan and Eisenstein, 2015. "You’re Mr. Lebowski, I’m the Dude”: Inducing Address Term Formality in Signed Social Networks. NAACL HLT.

• Iyyer, Guha, Chaturvedi, Boyd-Graber, Daume III, 2016. Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships. NAACL.

• Chaturvedi, Iyyer, Daumé III, 2017. Unsupervised Learning of Evolving Relationships Between Literary Characters. AAAI 2017.

General topics on Narrative Intelligence 

• Harrison and Riedl, 2016. Learning From Stories: Using Crowdsourced Narratives to Train Virtual Agents. AAAI.

• Riedl and Harrison, 2016. Using Stories to Teach Human Values to Artificial Agents. Workshop on AI, Ethics and Society.

• Mueller, 2004. Understanding script-based stories using commonsense reasoning. Cognitive Systems Research.

• Underwood, Bamman, and Lee, 2018. The Transformation of Gender in English-Language Fiction. Cultural Analytics

• Elson, 2012: Detecting story analogies from annotations of time, action and agency. LREC Workshop on Computational Models of Narrative.

• Chaturvedi, Srivastava, and Roth, 2018. Where Have I Heard This Story Before? Identifying Narrative Similarity in Movie Remakes.NAACL-HLT (short).

• Roemmele and Gordon, 2018. An Encoder-decoder Approach to Predicting Causal Relations in Stories. NAACL Workshop on Storytelling.

Natural Language Reasoning: Story cloze

• Cai, Tu, and Gimpel, 2017. Pay Attention to the Ending: Strong Neural Baselines for the ROC Story Cloze Task. ACL (short).

• Chaturvedi, Peng, and Roth, 2017. Story Comprehension for Predicting What Happens Next. EMNLP 2017.

• Radford, Narasimhan, Salimans, Sutskever. Improving Language Understanding by Generative Pre-Training.

• Chen, Chen, and Yu, 2018. Incorporating Structured Commonsense Knowledge in Story Completion. arXiv.

• Li, Li, Wei, Gu, Jatowt, and Yang, 2018. A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task. CoLing.

Creative Text Generation 

• McIntyre, and Lapata, 2009. Learning to tell tales: A data-driven approach to story generation. ACL.

• McIntyre, and Lapata, 2010. Plot induction and evolutionary search for story generation. ACL.

• Rishes, Lukin, Elson, and Walker, 2013. Generating different story tellings from semantic representations of narrative. ICIDS.

• Lukin, Reed, and Walker, 2015. Generating sentence planning variations for story telling. SIGDIAL.

• Harrison, Purdy, and Riedl, 2017. Toward Automated Story Generation with Markov Chain Monte Carlo Methods and Deep Neural Networks. Workshop on Intelligent Narrative Technologies.

• Martin, Ammanabrolu, Wang, Hancock, Singh, Harrison, and Riedl, 2018. Event Representations for Automated Story Generation with Deep Neural Nets. AAAI.

• Clark, Ji, and Smith, 2018. Neural Text Generation in Stories using Entity Representations as Context. NAACL.

• Lewis, Dauphin, Fan, 2018. Hierarchical Neural Story Generation. ACL. 

• Purdy, Wang, He, and Riedl, 2018. Predicting Generated Story Quality with Quantitative Measures. AIIDE. 

• Xu , Ren , Zhang , Zeng , Cai, and Sun, 2018. A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation. EMNLP.

• Yao, Peng, Weischedel, Knight, Zhao and Yan, 2019. Plan-And-Write: Towards Better Automatic Storytelling. AAAI.

Multi-modal NLP 

• Venugopalan, Xu, Donahue, Rohrbach, Mooney, and Saenko, 2015. Translating videos to natural language using deep recurrent neural networks. NAACL .

• Iyyer, Manjunatha, Guha, Vyas, Boyd-Graber, Daumé III, and Davis, 2017. The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives. CVPR.

• Gillick and Bamman, 2018. Telling Stories with Soundtracks: An Empirical Analysis of Music in Film. NAACL.