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21.
Cychosz, Margaret; Gordon, Andrew S.; Odimegwu, Obiageli; Connolly, Olivia; Bellassai, Jenna; Roemmele, Melissa
Effective Scenario Designs for Free-Text Interactive Fiction Proceedings Article
In: Proceedings of the International Conference on Interactive Digital Storytelling, pp. 12–23, Springer International Publishing, Funchal Madeira, Portugal, 2017.
@inproceedings{cychosz_effective_2017,
title = {Effective Scenario Designs for Free-Text Interactive Fiction},
author = {Margaret Cychosz and Andrew S. Gordon and Obiageli Odimegwu and Olivia Connolly and Jenna Bellassai and Melissa Roemmele},
url = {https://link.springer.com/chapter/10.1007/978-3-319-71027-3_2},
doi = {10.1007/978-3-319-71027-3_2},
year = {2017},
date = {2017-11-01},
booktitle = {Proceedings of the International Conference on Interactive Digital Storytelling},
pages = {12–23},
publisher = {Springer International Publishing},
address = {Funchal Madeira, Portugal},
abstract = {Free-text interactive fiction allows players to narrate the actions of protagonists via natural language input, which are automatically directed to appropriate storyline outcomes using natural language processing techniques. We describe an authoring platform called the Data-driven Interactive Narrative Engine (DINE), which supports free-text interactive fiction by connecting player input to authored outcomes using unsupervised text classification techniques based on text corpus statistics. We hypothesize that the coherence of the interaction, as judged by the players of a DINE scenario, is dependent on specific design choices made by the author. We describe three empirical experiments with crowdsourced subjects to investigate how authoring choices impacted the coherence of the interaction, finding that scenario design and writing style can predict significant differences.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Free-text interactive fiction allows players to narrate the actions of protagonists via natural language input, which are automatically directed to appropriate storyline outcomes using natural language processing techniques. We describe an authoring platform called the Data-driven Interactive Narrative Engine (DINE), which supports free-text interactive fiction by connecting player input to authored outcomes using unsupervised text classification techniques based on text corpus statistics. We hypothesize that the coherence of the interaction, as judged by the players of a DINE scenario, is dependent on specific design choices made by the author. We describe three empirical experiments with crowdsourced subjects to investigate how authoring choices impacted the coherence of the interaction, finding that scenario design and writing style can predict significant differences.
22.
Swanson, Reid William; Gordon, Andrew S.; Khooshabeh, Peter; Sagae, Kenji; Huskey, Richard; Mangus, Michael; Amir, Ori; Weber, Rene
An Empirical Analysis of Subjectivity and Narrative Levels in Weblog Storytelling Across Cultures Journal Article
In: Dialogue & Discourse, vol. 8, no. 2, pp. 105–128, 2017.
@article{swanson_empirical_2017,
title = {An Empirical Analysis of Subjectivity and Narrative Levels in Weblog Storytelling Across Cultures},
author = {Reid William Swanson and Andrew S. Gordon and Peter Khooshabeh and Kenji Sagae and Richard Huskey and Michael Mangus and Ori Amir and Rene Weber},
url = {https://www.researchgate.net/publication/321170929_An_Empirical_Analysis_of_Subjectivity_and_Narrative_Levels_in_Personal_Weblog_Storytelling_Across_Cultures?_sg=Ck1pqxhW1uuTUe54DX5BLVYey6L6DkwTpjnes1ctAEuGQDHxoEOr887eKWjHIA0_-kk4ya9dXwEZ4OM},
doi = {10.5087/dad.2017.205},
year = {2017},
date = {2017-11-01},
journal = {Dialogue & Discourse},
volume = {8},
number = {2},
pages = {105–128},
abstract = {Storytelling is a universal activity, but the way in which discourse structure is used to persuasively convey ideas and emotions may depend on cultural factors. Because first-person accounts of life experiences can have a powerful impact in how a person is perceived, the storyteller may instinctively employ specific strategies to shape the audience’s perception. Hypothesizing that some of the differences in storytelling can be captured by the use of narrative levels and subjectivity, we analyzed over one thousand narratives taken from personal weblogs. First, we compared stories from three different cultures written in their native languages: English, Chinese and Farsi. Second, we examined the impact of these two discourse properties on a reader’s attitude and behavior toward the narrator. We found surprising similarities and differences in how stories are structured along these two dimensions across cultures. These discourse properties have a small but significant impact on a reader’s behavioral response toward the narrator.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Storytelling is a universal activity, but the way in which discourse structure is used to persuasively convey ideas and emotions may depend on cultural factors. Because first-person accounts of life experiences can have a powerful impact in how a person is perceived, the storyteller may instinctively employ specific strategies to shape the audience’s perception. Hypothesizing that some of the differences in storytelling can be captured by the use of narrative levels and subjectivity, we analyzed over one thousand narratives taken from personal weblogs. First, we compared stories from three different cultures written in their native languages: English, Chinese and Farsi. Second, we examined the impact of these two discourse properties on a reader’s attitude and behavior toward the narrator. We found surprising similarities and differences in how stories are structured along these two dimensions across cultures. These discourse properties have a small but significant impact on a reader’s behavioral response toward the narrator.
23.
Prasad, Keshav; Briet, Kayla; Odimegwu, Obiageli; Connolly, Olivia; Gonzalez, Diego; Gordon, Andrew S.
“The Long Walk” From Linear Film to Interactive Narrative Proceedings Article
In: Proceedings of the 10th International Workshop on Intelligent Narrative Technologies (INT10), AAAI, Snowbird, Utah, 2017.
@inproceedings{prasad_long_2017,
title = {“The Long Walk” From Linear Film to Interactive Narrative},
author = {Keshav Prasad and Kayla Briet and Obiageli Odimegwu and Olivia Connolly and Diego Gonzalez and Andrew S. Gordon},
url = {http://people.ict.usc.edu/ gordon/publications/INT17B},
year = {2017},
date = {2017-10-01},
booktitle = {Proceedings of the 10th International Workshop on Intelligent Narrative Technologies (INT10)},
publisher = {AAAI},
address = {Snowbird, Utah},
abstract = {Advances in hardware and software for virtual reality and 360-degree video afford new opportunities for immersive digital storytelling, but also pose new challenges as players seek an increased sense of meaningful agency in fictional storyworlds. In this paper, we explore the interaction designs afforded by voice-controlled interactive narratives, where players speak their intended actions when prompted at choice points in branching storylines. We describe seven interaction design patterns that balance the player’s need for meaningful agency with an author’s goal to present an intended storyline. We argue that these structural designs are orthogonal to the content of a story, such that any particular story may be effectively restructured to use different patterns. By way of demonstration, we describe our efforts to remix and restructure a 360-degree film entitled The Long Walk, transforming it from a largely linear narrative with minimal interactivity into a voice-controlled interactive narrative with meaningful player agency.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Advances in hardware and software for virtual reality and 360-degree video afford new opportunities for immersive digital storytelling, but also pose new challenges as players seek an increased sense of meaningful agency in fictional storyworlds. In this paper, we explore the interaction designs afforded by voice-controlled interactive narratives, where players speak their intended actions when prompted at choice points in branching storylines. We describe seven interaction design patterns that balance the player’s need for meaningful agency with an author’s goal to present an intended storyline. We argue that these structural designs are orthogonal to the content of a story, such that any particular story may be effectively restructured to use different patterns. By way of demonstration, we describe our efforts to remix and restructure a 360-degree film entitled The Long Walk, transforming it from a largely linear narrative with minimal interactivity into a voice-controlled interactive narrative with meaningful player agency.
24.
Treanor, Mike; Warren, Nicholas; Reed, Mason; Smith, Adam M.; Ortiz, Pablo; Carney, Laurel; Sherman, Loren; Carré, Elizabeth; Vivatvisha, Nadya; Harrell, D. Fox; Mardo, Paola; Gordon, Andrew; Dormans, Joris; Robison, Barrie; Gomez, Spencer; Heck, Samantha; Wright, Landon; Soule, Terence
Playable Experiences at AIIDE 2017 Proceedings Article
In: Proceedings of The Thirteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-17), Association for the Advancement of Artificial Intelligence, Snowbird, Utah, 2017.
@inproceedings{treanor_playable_2017,
title = {Playable Experiences at AIIDE 2017},
author = {Mike Treanor and Nicholas Warren and Mason Reed and Adam M. Smith and Pablo Ortiz and Laurel Carney and Loren Sherman and Elizabeth Carré and Nadya Vivatvisha and D. Fox Harrell and Paola Mardo and Andrew Gordon and Joris Dormans and Barrie Robison and Spencer Gomez and Samantha Heck and Landon Wright and Terence Soule},
url = {https://pdfs.semanticscholar.org/19f9/a76f6edcc6aa41bf19dba017da8c1c01e2b3.pdf},
year = {2017},
date = {2017-10-01},
booktitle = {Proceedings of The Thirteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-17)},
publisher = {Association for the Advancement of Artificial Intelligence},
address = {Snowbird, Utah},
abstract = {This paper describes the accepted entries to the fifth Playable Experiences track to be held at the AIIDE conference. The Playable Experiences track showcases complete works that make use of artificial intelligence techniques as an integral part of the player experience.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper describes the accepted entries to the fifth Playable Experiences track to be held at the AIIDE conference. The Playable Experiences track showcases complete works that make use of artificial intelligence techniques as an integral part of the player experience.
25.
Bellassai, Jenna; Gordon, Andrew S.; Roemmele, Melissa; Cychosz, Margaret; Odimegwu, Obiageli; Connolly, Olivia
Unsupervised Text Classification for Natural Language Interactive Narratives Proceedings Article
In: Proceedings of the 10th International Workshop on Intelligent Narrative Technologies (INT10), AAAI, Snowbird, Utah, 2017.
@inproceedings{bellassai_unsupervised_2017,
title = {Unsupervised Text Classification for Natural Language Interactive Narratives},
author = {Jenna Bellassai and Andrew S. Gordon and Melissa Roemmele and Margaret Cychosz and Obiageli Odimegwu and Olivia Connolly},
url = {http://people.ict.usc.edu/ gordon/publications/INT17A},
year = {2017},
date = {2017-10-01},
booktitle = {Proceedings of the 10th International Workshop on Intelligent Narrative Technologies (INT10)},
publisher = {AAAI},
address = {Snowbird, Utah},
abstract = {Natural language interactive narratives are a variant of traditional branching storylines where player actions are expressed in natural language rather than by selecting among choices. Previous efforts have handled the richness of natural language input using machine learning technologies for text classification, bootstrapping supervised machine learning approaches with human-in-the-loop data acquisition or by using expected player input as fake training data. This paper explores a third alternative, where unsupervised text classifiers are used to automatically route player input to the most appropriate storyline branch.We describe the Data-driven Interactive Narrative Engine (DINE), a web-based tool for authoring and deploying natural language interactive narratives. To compare the performance of different algorithms for unsupervised text classification, we collected thousands of user inputs from hundreds of crowdsourced participants playing 25 different scenarios, and hand-annotated them to create a goldstandard test set. Through comparative evaluations, we identified an unsupervised algorithm for narrative text classification that approaches the performance of supervised text classification algorithms. We discuss how this technology supports authors in the rapid creation and deployment of interactive narrative experiences, with authorial burdens similar to that of traditional branching storylines.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Natural language interactive narratives are a variant of traditional branching storylines where player actions are expressed in natural language rather than by selecting among choices. Previous efforts have handled the richness of natural language input using machine learning technologies for text classification, bootstrapping supervised machine learning approaches with human-in-the-loop data acquisition or by using expected player input as fake training data. This paper explores a third alternative, where unsupervised text classifiers are used to automatically route player input to the most appropriate storyline branch.We describe the Data-driven Interactive Narrative Engine (DINE), a web-based tool for authoring and deploying natural language interactive narratives. To compare the performance of different algorithms for unsupervised text classification, we collected thousands of user inputs from hundreds of crowdsourced participants playing 25 different scenarios, and hand-annotated them to create a goldstandard test set. Through comparative evaluations, we identified an unsupervised algorithm for narrative text classification that approaches the performance of supervised text classification algorithms. We discuss how this technology supports authors in the rapid creation and deployment of interactive narrative experiences, with authorial burdens similar to that of traditional branching storylines.
26.
Gordon, Andrew S.; Hobbs, Jerry R.
A Formal Theory of Commonsense Psychology: How People Think People Think Book
Cambridge University Press, Cambridge, UK, 2017, ISBN: 978-1-108-50963-3.
@book{gordon_formal_2017,
title = {A Formal Theory of Commonsense Psychology: How People Think People Think},
author = {Andrew S. Gordon and Jerry R. Hobbs},
url = {https://books.google.com/books?id=OEY3DwAAQBAJ&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false},
isbn = {978-1-108-50963-3},
year = {2017},
date = {2017-09-01},
publisher = {Cambridge University Press},
address = {Cambridge, UK},
abstract = {Commonsense psychology refers to the implicit theories that we all use to make sense of people's behavior in terms of their beliefs, goals, plans, and emotions. These are also the theories we employ when we anthropomorphize complex machines and computers as if they had humanlike mental lives. In order to successfully cooperate and communicate with people, these theories will need to be represented explicitly in future artificial intelligence systems. This book provides a large-scale logical formalization of commonsense psychology in support of humanlike artificial intelligence. It uses formal logic to encode the deep lexical semantics of the full breadth of psychological words and phrases, providing fourteen hundred axioms of first-order logic organized into twenty-nine commonsense psychology theories and sixteen background theories. This in-depth exploration of human commonsense reasoning for artificial intelligence researchers, linguists, and cognitive and social psychologists will serve as a foundation for the development of humanlike artificial intelligence.},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
Commonsense psychology refers to the implicit theories that we all use to make sense of people's behavior in terms of their beliefs, goals, plans, and emotions. These are also the theories we employ when we anthropomorphize complex machines and computers as if they had humanlike mental lives. In order to successfully cooperate and communicate with people, these theories will need to be represented explicitly in future artificial intelligence systems. This book provides a large-scale logical formalization of commonsense psychology in support of humanlike artificial intelligence. It uses formal logic to encode the deep lexical semantics of the full breadth of psychological words and phrases, providing fourteen hundred axioms of first-order logic organized into twenty-nine commonsense psychology theories and sixteen background theories. This in-depth exploration of human commonsense reasoning for artificial intelligence researchers, linguists, and cognitive and social psychologists will serve as a foundation for the development of humanlike artificial intelligence.
27.
Roemmele, Melissa; Mardo, Paola; Gordon, Andrew S.
Natural-language Interactive Narratives in Imaginal Exposure Therapy for Obsessive-Compulsive Disorder Proceedings Article
In: Proceedings of the Computational Linguistics and Clinical Psychology Workshop (CLPsych), pp. 48–57, Association for Computational Linguistics, Vancouver, Canada, 2017.
@inproceedings{roemmele_natural-language_2017,
title = {Natural-language Interactive Narratives in Imaginal Exposure Therapy for Obsessive-Compulsive Disorder},
author = {Melissa Roemmele and Paola Mardo and Andrew S. Gordon},
url = {http://www.aclweb.org/anthology/W17-31#page=58},
year = {2017},
date = {2017-08-01},
booktitle = {Proceedings of the Computational Linguistics and Clinical Psychology Workshop (CLPsych)},
pages = {48–57},
publisher = {Association for Computational Linguistics},
address = {Vancouver, Canada},
abstract = {Obsessive-compulsive disorder (OCD) is an anxiety-based disorder that affects around 2.5% of the population. A common treatment for OCD is exposure therapy, where the patient repeatedly confronts a feared experience, which has the long-term effect of decreasing their anxiety. Some exposures consist of reading and writing stories about an imagined anxiety-provoking scenario. In this paper, we present a technology that enables patients to interactively contribute to exposure stories by supplying natural language input (typed or spoken) that advances a scenario. This interactivity could potentially increase the patient’s sense of immersion in an exposure and contribute to its success. We introduce the NLP task behind processing inputs to predict new events in the scenario, and describe our initial approach. We then illustrate the future possibility of this work with an example of an exposure scenario authored with our application.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Obsessive-compulsive disorder (OCD) is an anxiety-based disorder that affects around 2.5% of the population. A common treatment for OCD is exposure therapy, where the patient repeatedly confronts a feared experience, which has the long-term effect of decreasing their anxiety. Some exposures consist of reading and writing stories about an imagined anxiety-provoking scenario. In this paper, we present a technology that enables patients to interactively contribute to exposure stories by supplying natural language input (typed or spoken) that advances a scenario. This interactivity could potentially increase the patient’s sense of immersion in an exposure and contribute to its success. We introduce the NLP task behind processing inputs to predict new events in the scenario, and describe our initial approach. We then illustrate the future possibility of this work with an example of an exposure scenario authored with our application.
28.
Roemmele, Melissa; Gordon, Andrew S.; Swanson, Reid
Evaluating Story Generation Systems Using Automated Linguistic Analyses Proceedings Article
In: Proceedings of the SIGKDD-2017 Workshop on Machine Learning for Creativity, ACM, Halifax, Nova Scotia, Canada, 2017.
@inproceedings{roemmele_evaluating_2017,
title = {Evaluating Story Generation Systems Using Automated Linguistic Analyses},
author = {Melissa Roemmele and Andrew S. Gordon and Reid Swanson},
url = {http://people.ict.usc.edu/ roemmele/publications/fiction_generation.pdf},
year = {2017},
date = {2017-08-01},
booktitle = {Proceedings of the SIGKDD-2017 Workshop on Machine Learning for Creativity},
publisher = {ACM},
address = {Halifax, Nova Scotia, Canada},
abstract = {Story generation is a well-recognized task in computational creativity research, but one that can be di cult to evaluate empirically. It is often ine cient and costly to rely solely on human feedback for judging the quality of generated stories. We address this by examining the use of linguistic analyses for automated evaluation, using metrics from existing work on predicting writing quality. We apply these metrics speci cally to story continuation, where a model is given the beginning of a story and generates the next sentence, which is useful for systems that interactively support authors' creativity in writing. We compare sentences generated by different existing models to human-authored ones according to the analyses. The results show some meaningful dfferences between the models, suggesting that this evaluation approach may be advantageous for future research.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Story generation is a well-recognized task in computational creativity research, but one that can be di cult to evaluate empirically. It is often ine cient and costly to rely solely on human feedback for judging the quality of generated stories. We address this by examining the use of linguistic analyses for automated evaluation, using metrics from existing work on predicting writing quality. We apply these metrics speci cally to story continuation, where a model is given the beginning of a story and generates the next sentence, which is useful for systems that interactively support authors' creativity in writing. We compare sentences generated by different existing models to human-authored ones according to the analyses. The results show some meaningful dfferences between the models, suggesting that this evaluation approach may be advantageous for future research.
29.
Gordon, Andrew S
Solving Interpretation Problems With Etcetera Abduction Proceedings Article
In: Proceedings of the Fifth Annual Conference on Advances in Cognitive Systems, 2014 Cognitive Systems Foundation, Troy, New York, 2017.
@inproceedings{gordon_solving_2017,
title = {Solving Interpretation Problems With Etcetera Abduction},
author = {Andrew S Gordon},
url = {http://people.ict.usc.edu/ gordon/publications/ACS17.PDF},
year = {2017},
date = {2017-05-01},
booktitle = {Proceedings of the Fifth Annual Conference on Advances in Cognitive Systems},
publisher = {2014 Cognitive Systems Foundation},
address = {Troy, New York},
abstract = {Among the most challenging problems in Artificial Intelligence are those that require human-like abilities to make sense of ambiguous observations, to interpret events in context given a wealth of life experiences and commonsense knowledge. In the 1990s, Jerry Hobbs and colleagues demonstrated how interpretation problems can be tackled with logical abduction, a combinatorial search for the best set of assumptions that logically entails the observations. Etcetera Abduction is a new approach to ranking assumptions by reifying the uncertainty of knowledge base axioms as etcetera literals, representing conditional and prior probabilities that can be combined through logical unification. In this invited talk, I will highlight some of the features of Etcetera Abduction that make it attractive compared to alternatives, and share my perspective on the role of logic-based reasoning given current trends in machine learning research.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Among the most challenging problems in Artificial Intelligence are those that require human-like abilities to make sense of ambiguous observations, to interpret events in context given a wealth of life experiences and commonsense knowledge. In the 1990s, Jerry Hobbs and colleagues demonstrated how interpretation problems can be tackled with logical abduction, a combinatorial search for the best set of assumptions that logically entails the observations. Etcetera Abduction is a new approach to ranking assumptions by reifying the uncertainty of knowledge base axioms as etcetera literals, representing conditional and prior probabilities that can be combined through logical unification. In this invited talk, I will highlight some of the features of Etcetera Abduction that make it attractive compared to alternatives, and share my perspective on the role of logic-based reasoning given current trends in machine learning research.
30.
Inoue, Naoya; Gordon, Andrew S.
A Scalable Weighted Max-SAT Implementation of Propositional Etcetera Abduction Proceedings Article
In: Proceedings of the 30th International Conference of the Florida AI Society (FLAIRS-30), AAAI Press, Marco Island, Florida, 2017.
@inproceedings{inoue_scalable_2017,
title = {A Scalable Weighted Max-SAT Implementation of Propositional Etcetera Abduction},
author = {Naoya Inoue and Andrew S. Gordon},
url = {http://people.ict.usc.edu/ gordon/publications/FLAIRS17.PDF},
year = {2017},
date = {2017-05-01},
booktitle = {Proceedings of the 30th International Conference of the Florida AI Society (FLAIRS-30)},
publisher = {AAAI Press},
address = {Marco Island, Florida},
abstract = {Recent advances in technology for abductive reasoning, or inference to the best explanation, encourage the application of abduction to real-life commonsense reasoning problems. This paper describes Etcetera Abduction, a new implementation of logical abduction that is both grounded in probability theory and optimized using contemporary linear programming solvers. We present a Weighted Max-SAT formulation of Etcetera Abduction, which allows us to exploit highly advanced technologies developed in the field of SAT and Operations Research. Our experiments demonstrate the scalability of our proposal on a large-scale synthetic benchmark that contains up to ten thousand axioms, using one of the stateof-the-art mathematical optimizers developed in these fields. This is the first work to evaluate a SAT-based approach to abductive reasoning at this scale. The inference engine we developed has been made publicly available.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent advances in technology for abductive reasoning, or inference to the best explanation, encourage the application of abduction to real-life commonsense reasoning problems. This paper describes Etcetera Abduction, a new implementation of logical abduction that is both grounded in probability theory and optimized using contemporary linear programming solvers. We present a Weighted Max-SAT formulation of Etcetera Abduction, which allows us to exploit highly advanced technologies developed in the field of SAT and Operations Research. Our experiments demonstrate the scalability of our proposal on a large-scale synthetic benchmark that contains up to ten thousand axioms, using one of the stateof-the-art mathematical optimizers developed in these fields. This is the first work to evaluate a SAT-based approach to abductive reasoning at this scale. The inference engine we developed has been made publicly available.
31.
Roemmele, Melissa; Kobayashi, Sosuke; Inoue, Naoya; Gordon, Andrew M.
An RNN-based Binary Classifier for the Story Cloze Test Proceedings Article
In: Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, pp. 74–80, Association for Computational Linguistics, Valencia, Spain, 2017.
@inproceedings{roemmele_rnn-based_2017,
title = {An RNN-based Binary Classifier for the Story Cloze Test},
author = {Melissa Roemmele and Sosuke Kobayashi and Naoya Inoue and Andrew M. Gordon},
url = {http://www.aclweb.org/anthology/W/W17/W17-09.pdf#page=86},
year = {2017},
date = {2017-04-01},
booktitle = {Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics},
pages = {74–80},
publisher = {Association for Computational Linguistics},
address = {Valencia, Spain},
abstract = {The Story Cloze Test consists of choosing a sentence that best completes a story given two choices. In this paper we present a system that performs this task using a supervised binary classifier on top of a recurrent neural network to predict the probability that a given story ending is correct. The classifier is trained to distinguish correct story endings given in the training data from incorrect ones that we artificially generate. Our experiments evaluate different methods for generating these negative examples, as well as different embedding-based representations of the stories. Our best result obtains 67.2% accuracy on the test set, outperforming the existing top baseline of 58.5%.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The Story Cloze Test consists of choosing a sentence that best completes a story given two choices. In this paper we present a system that performs this task using a supervised binary classifier on top of a recurrent neural network to predict the probability that a given story ending is correct. The classifier is trained to distinguish correct story endings given in the training data from incorrect ones that we artificially generate. Our experiments evaluate different methods for generating these negative examples, as well as different embedding-based representations of the stories. Our best result obtains 67.2% accuracy on the test set, outperforming the existing top baseline of 58.5%.
32.
Dehghani, Morteza; Boghrati, Reihane; Man, Kingson; Hoover, Joseph; Gimbel, Sarah; Vaswani, Ashish; Zevin, Jason; Immordino, Mary Helen; Gordon, Andrew; Damasio, Antonio; Kaplan, Jonas T.
Decoding the Neural Representation of Story Meanings across Languages Journal Article
In: Human Brain Mapping, vol. 38, no. 12, 2017.
@article{dehghani_decoding_2017,
title = {Decoding the Neural Representation of Story Meanings across Languages},
author = {Morteza Dehghani and Reihane Boghrati and Kingson Man and Joseph Hoover and Sarah Gimbel and Ashish Vaswani and Jason Zevin and Mary Helen Immordino and Andrew Gordon and Antonio Damasio and Jonas T. Kaplan},
url = {https://psyarxiv.com/qrpp3/},
doi = {10.1002/hbm.23814},
year = {2017},
date = {2017-03-01},
journal = {Human Brain Mapping},
volume = {38},
number = {12},
abstract = {Drawing from a common lexicon of semantic units, humans fashion narratives whose meaning transcends that of their individual utterances. However, while brain regions that represent lower-level semantic units, such as words and sentences, have been identified, questions remain about the neural representation of narrative comprehension, which involves inferring cumulative meaning. To address these questions, we exposed English, Mandarin and Farsi native speakers to native language translations of the same stories during fMRI scanning. Using a new technique in natural language processing, we calculated the distributed representations of these stories (capturing the meaning of the stories in high-dimensional semantic space), and demonstrate that using these representations we can identify the specific story a participant was reading from the neural data. Notably, this was possible even when the distributed representations were calculated using stories in a different language than the participant was reading. Relying on over 44 billion classifications, our results reveal that identification relied on a collection of brain regions most prominently located in the default mode network. These results demonstrate that neuro-semantic encoding of narratives happens at levels higher than individual semantic units and that this encoding is systematic across both individuals and languages.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Drawing from a common lexicon of semantic units, humans fashion narratives whose meaning transcends that of their individual utterances. However, while brain regions that represent lower-level semantic units, such as words and sentences, have been identified, questions remain about the neural representation of narrative comprehension, which involves inferring cumulative meaning. To address these questions, we exposed English, Mandarin and Farsi native speakers to native language translations of the same stories during fMRI scanning. Using a new technique in natural language processing, we calculated the distributed representations of these stories (capturing the meaning of the stories in high-dimensional semantic space), and demonstrate that using these representations we can identify the specific story a participant was reading from the neural data. Notably, this was possible even when the distributed representations were calculated using stories in a different language than the participant was reading. Relying on over 44 billion classifications, our results reveal that identification relied on a collection of brain regions most prominently located in the default mode network. These results demonstrate that neuro-semantic encoding of narratives happens at levels higher than individual semantic units and that this encoding is systematic across both individuals and languages.
33.
Nack, Frank; Gordon, Andrew S. (Ed.)
Springer International Publishing, Cham, Switzerland, 2016, ISBN: 978-3-319-48278-1 978-3-319-48279-8.
@book{nack_interactive_2016,
title = {Interactive Storytelling},
editor = {Frank Nack and Andrew S. Gordon},
url = {http://link.springer.com/10.1007/978-3-319-48279-8},
doi = {10.1007/978-3-319-48279-8},
isbn = {978-3-319-48278-1 978-3-319-48279-8},
year = {2016},
date = {2016-11-01},
volume = {10045},
publisher = {Springer International Publishing},
address = {Cham, Switzerland},
series = {Lecture Notes in Computer Science},
abstract = {This book constitutes the refereed proceedings of the 9th International Conference on Interactive Digital Storytelling, ICIDS 2016, held in Los Angeles, CA, USA, in November 2016. The 26 revised full papers and 8 short papers presented together with 9 posters, 4 workshop, and 3 demonstration papers were carefully reviewed and selected from 88 submissions. The papers are organized in topical sections on analyses and evaluation systems; brave new ideas; intelligent narrative technologies; theoretical foundations; and usage scenarios and applications.},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
This book constitutes the refereed proceedings of the 9th International Conference on Interactive Digital Storytelling, ICIDS 2016, held in Los Angeles, CA, USA, in November 2016. The 26 revised full papers and 8 short papers presented together with 9 posters, 4 workshop, and 3 demonstration papers were carefully reviewed and selected from 88 submissions. The papers are organized in topical sections on analyses and evaluation systems; brave new ideas; intelligent narrative technologies; theoretical foundations; and usage scenarios and applications.
34.
Ryan, James; Swanson, Reid
Recognizing Coherent Narrative Blog Content Proceedings Article
In: Proceeedings of the International Conference on Interactive Digital Storytelling, pp. 234–246, Springer International Publishing, Cham, Switzerland, 2016, ISBN: 978-3-319-48278-1 978-3-319-48279-8.
@inproceedings{ryan_recognizing_2016,
title = {Recognizing Coherent Narrative Blog Content},
author = {James Ryan and Reid Swanson},
url = {http://link.springer.com/10.1007/978-3-319-48279-8_21},
doi = {10.1007/978-3-319-48279-8_21},
isbn = {978-3-319-48278-1 978-3-319-48279-8},
year = {2016},
date = {2016-10-01},
booktitle = {Proceeedings of the International Conference on Interactive Digital Storytelling},
pages = {234–246},
publisher = {Springer International Publishing},
address = {Cham, Switzerland},
abstract = {Interactive storytelling applications have at their disposal massive numbers of human-authored stories, in the form of narrative weblog posts, from which story content could be harvested and repurposed. Such repurposing is currently inhibited, however, in that many blog narratives are not sufficiently coherent for use in these applications. In a narrative that is not coherent, the order of the events in the narrative is not clear given the text of the story. We present the results of a study exploring automatic methods for estimating the coherence of narrative blog posts. In the end, our simplest model—one that only considers the degree to which story text is capitalized and punctuated—vastly outperformed a baseline model and, curiously, a series of more sophisticated models. Future work may use this simple model as a baseline, or may use it along with the classifier that it extends to automatically extract large numbers of narrative blog posts from the web for purposes such as interactive storytelling.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Interactive storytelling applications have at their disposal massive numbers of human-authored stories, in the form of narrative weblog posts, from which story content could be harvested and repurposed. Such repurposing is currently inhibited, however, in that many blog narratives are not sufficiently coherent for use in these applications. In a narrative that is not coherent, the order of the events in the narrative is not clear given the text of the story. We present the results of a study exploring automatic methods for estimating the coherence of narrative blog posts. In the end, our simplest model—one that only considers the degree to which story text is capitalized and punctuated—vastly outperformed a baseline model and, curiously, a series of more sophisticated models. Future work may use this simple model as a baseline, or may use it along with the classifier that it extends to automatically extract large numbers of narrative blog posts from the web for purposes such as interactive storytelling.
35.
Ahn, Emily; Morbini, Fabrizio; Gordon, Andrew S.
Improving Fluency in Narrative Text Generation With Grammatical Transformations and Probabilistic Parsing Proceedings Article
In: Proceedings of the 9th International Natural Language Generation Conference (INLG-2016), Edinburgh, UK, 2016.
@inproceedings{ahn_improving_2016,
title = {Improving Fluency in Narrative Text Generation With Grammatical Transformations and Probabilistic Parsing},
author = {Emily Ahn and Fabrizio Morbini and Andrew S. Gordon},
url = {https://www.researchgate.net/publication/307512031_Improving_Fluency_in_Narrative_Text_Generation_With_Grammatical_Transformations_and_Probabilistic_Parsing},
year = {2016},
date = {2016-09-01},
booktitle = {Proceedings of the 9th International Natural Language Generation Conference (INLG-2016)},
address = {Edinburgh, UK},
abstract = {In research on automatic generation of narrative text, story events are often formally represented as a causal graph. When serializing and realizing this causal graph as natural language text, simple approaches produce cumbersome sentences with repetitive syntactic structure, e.g. long chains of “because” clauses. In our research, we show that the fluency of narrative text generated from causal graphs can be improved by applying rule-based grammatical transformations to generate many sentence variations with equivalent semantics, then selecting the variation that has the highest probability using a probabilistic syntactic parser. We evaluate our approach by generating narrative text from causal graphs that encode 100 brief stories involving the same three characters, based on a classic film of experimental social psychology. Crowdsourced workers judged the writing quality of texts generated with ranked transformations as significantly higher than those without, and not significantly lower than human-authored narratives of the same situations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In research on automatic generation of narrative text, story events are often formally represented as a causal graph. When serializing and realizing this causal graph as natural language text, simple approaches produce cumbersome sentences with repetitive syntactic structure, e.g. long chains of “because” clauses. In our research, we show that the fluency of narrative text generated from causal graphs can be improved by applying rule-based grammatical transformations to generate many sentence variations with equivalent semantics, then selecting the variation that has the highest probability using a probabilistic syntactic parser. We evaluate our approach by generating narrative text from causal graphs that encode 100 brief stories involving the same three characters, based on a classic film of experimental social psychology. Crowdsourced workers judged the writing quality of texts generated with ranked transformations as significantly higher than those without, and not significantly lower than human-authored narratives of the same situations.
36.
Roemmele, Melissa; Morgens, Soja-Marie; Gordon, Andrew S.; Morency, Louis-Philippe
Recognizing Human Actions in the Motion Trajectories of Shapes Proceedings Article
In: Proceedings of ACM Intelligent User Interfaces, pp. 271–281, ACM Press, Sonoma, CA, 2016, ISBN: 978-1-4503-4137-0.
@inproceedings{roemmele_recognizing_2016,
title = {Recognizing Human Actions in the Motion Trajectories of Shapes},
author = {Melissa Roemmele and Soja-Marie Morgens and Andrew S. Gordon and Louis-Philippe Morency},
url = {http://dl.acm.org/citation.cfm?id=2856793},
doi = {10.1145/2856767.2856793},
isbn = {978-1-4503-4137-0},
year = {2016},
date = {2016-03-01},
booktitle = {Proceedings of ACM Intelligent User Interfaces},
pages = {271–281},
publisher = {ACM Press},
address = {Sonoma, CA},
abstract = {People naturally anthropomorphize the movement of nonliving objects, as social psychologists Fritz Heider and Marianne Simmel demonstrated in their influential 1944 research study. When they asked participants to narrate an animated film of two triangles and a circle moving in and around a box, participants described the shapes' movement in terms of human actions. Using a framework for authoring and annotating animations in the style of Heider and Simmel, we established new crowdsourced datasets where the motion trajectories of animated shapes are labeled according to the actions they depict. We applied two machine learning approaches, a spatial-temporal bag-of-words model and a recurrent neural network, to the task of automatically recognizing actions in these datasets. Our best results outperformed a majority baseline and showed similarity to human performance, which encourages further use of these datasets for modeling perception from motion trajectories. Future progress on simulating human-like motion perception will require models that integrate motion information with top-down contextual knowledge.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
People naturally anthropomorphize the movement of nonliving objects, as social psychologists Fritz Heider and Marianne Simmel demonstrated in their influential 1944 research study. When they asked participants to narrate an animated film of two triangles and a circle moving in and around a box, participants described the shapes' movement in terms of human actions. Using a framework for authoring and annotating animations in the style of Heider and Simmel, we established new crowdsourced datasets where the motion trajectories of animated shapes are labeled according to the actions they depict. We applied two machine learning approaches, a spatial-temporal bag-of-words model and a recurrent neural network, to the task of automatically recognizing actions in these datasets. Our best results outperformed a majority baseline and showed similarity to human performance, which encourages further use of these datasets for modeling perception from motion trajectories. Future progress on simulating human-like motion perception will require models that integrate motion information with top-down contextual knowledge.
37.
Gordon, Andrew S.
Commonsense Interpretation of Triangle Behavior Proceedings Article
In: Thirtieth AAAI Conference on Artificial Intelligence, AAAI Press, Phoenix, AZ, 2016.
@inproceedings{gordon_commonsense_2016,
title = {Commonsense Interpretation of Triangle Behavior},
author = {Andrew S. Gordon},
url = {https://www.aaai.org/ocs/index.php/AAAI/AAAI16/rt/metadata/11790/12152},
year = {2016},
date = {2016-02-01},
booktitle = {Thirtieth AAAI Conference on Artificial Intelligence},
publisher = {AAAI Press},
address = {Phoenix, AZ},
abstract = {The ability to infer intentions, emotions, and other unobservable psychological states from people’s behavior is a hallmark of human social cognition, and an essential capability for future Artificial Intelligence systems. The commonsense theories of psychology and sociology necessary for such inferences have been a focus of logic-based knowledge representation research, but have been difficult to employ in robust automated reasoning architectures. In this paper we model behavior interpretation as a process of logical abduction, where the reasoning task is to identify the most probable set of assumptions that logically entail the observable behavior of others, given commonsense theories of psychology and sociology. We evaluate our approach using Triangle-COPA, a benchmark suite of 100 challenge problems based on an early social psychology experiment by Fritz Heider and Marianne Simmel. Commonsense knowledge of actions, social relationships, intentions, and emotions are encoded as defeasible axioms in first-order logic. We identify sets of assumptions that logically entail observed behaviors by backchaining with these axioms to a given depth, and order these sets by their joint probability assuming conditional independence. Our approach solves almost all (91) of the 100 questions in Triangle-COPA, and demonstrates a promising approach to robust behavior interpretation that integrates both logical and probabilistic reasoning.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The ability to infer intentions, emotions, and other unobservable psychological states from people’s behavior is a hallmark of human social cognition, and an essential capability for future Artificial Intelligence systems. The commonsense theories of psychology and sociology necessary for such inferences have been a focus of logic-based knowledge representation research, but have been difficult to employ in robust automated reasoning architectures. In this paper we model behavior interpretation as a process of logical abduction, where the reasoning task is to identify the most probable set of assumptions that logically entail the observable behavior of others, given commonsense theories of psychology and sociology. We evaluate our approach using Triangle-COPA, a benchmark suite of 100 challenge problems based on an early social psychology experiment by Fritz Heider and Marianne Simmel. Commonsense knowledge of actions, social relationships, intentions, and emotions are encoded as defeasible axioms in first-order logic. We identify sets of assumptions that logically entail observed behaviors by backchaining with these axioms to a given depth, and order these sets by their joint probability assuming conditional independence. Our approach solves almost all (91) of the 100 questions in Triangle-COPA, and demonstrates a promising approach to robust behavior interpretation that integrates both logical and probabilistic reasoning.
38.
Roemmele, Melissa
Writing Stories with Help from Recurrent Neural Networks Proceedings Article
In: AAAI Conference on Artificial Intelligence; Thirtieth AAAI Conference on Artificial Intelligence, pp. 4311 – 4312, AAAI Press, Phoenix, AZ, 2016.
@inproceedings{roemmele_writing_2016,
title = {Writing Stories with Help from Recurrent Neural Networks},
author = {Melissa Roemmele},
url = {http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11966},
year = {2016},
date = {2016-02-01},
booktitle = {AAAI Conference on Artificial Intelligence; Thirtieth AAAI Conference on Artificial Intelligence},
pages = {4311 – 4312},
publisher = {AAAI Press},
address = {Phoenix, AZ},
abstract = {This thesis explores the use of a recurrent neural network model for a novel story generation task. In this task, the model analyzes an ongoing story and generates a sentence that continues the story.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This thesis explores the use of a recurrent neural network model for a novel story generation task. In this task, the model analyzes an ongoing story and generates a sentence that continues the story.
39.
Kaplan, Jonas T.; Gimbel, Sarah I.; Dehghani, Morteza; Immordino-Yang, Mary Helen; Sagae, Kenji; Wong, Jennifer D.; Tipper, Christine M.; Damasio, Hanna; Gordon, Andrew S.; Damasio, Antonio
Processing Narratives Concerning Protected Values: A Cross-Cultural Investigation of Neural Correlates Journal Article
In: Cerebral Cortex, 2016, ISSN: 1047-3211, 1460-2199.
@article{kaplan_processing_2016,
title = {Processing Narratives Concerning Protected Values: A Cross-Cultural Investigation of Neural Correlates},
author = {Jonas T. Kaplan and Sarah I. Gimbel and Morteza Dehghani and Mary Helen Immordino-Yang and Kenji Sagae and Jennifer D. Wong and Christine M. Tipper and Hanna Damasio and Andrew S. Gordon and Antonio Damasio},
url = {http://www.cercor.oxfordjournals.org/lookup/doi/10.1093/cercor/bhv325},
doi = {10.1093/cercor/bhv325},
issn = {1047-3211, 1460-2199},
year = {2016},
date = {2016-01-01},
journal = {Cerebral Cortex},
abstract = {Narratives are an important component of culture and play a central role in transmitting social values. Little is known, however, about how the brain of a listener/reader processes narratives. A receiver's response to narration is influenced by the narrator's framing and appeal to values. Narratives that appeal to “protected values,” including core personal, national, or religious values, may be particularly effective at influencing receivers. Protected values resist compromise and are tied with identity, affective value, moral decision-making, and other aspects of social cognition. Here, we investigated the neural mechanisms underlying reactions to protected values in narratives. During fMRI scanning, we presented 78 American, Chinese, and Iranian participants with real-life stories distilled from a corpus of over 20 million weblogs. Reading these stories engaged the posterior medial, medial prefrontal, and temporo-parietal cortices. When participants believed that the protagonist was appealing to a protected value, signal in these regions was increased compared with when no protected value was perceived, possibly reflecting the intensive and iterative search required to process this material. The effect strength also varied across groups, potentially reflecting cultural differences in the degree of concern for protected values.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Narratives are an important component of culture and play a central role in transmitting social values. Little is known, however, about how the brain of a listener/reader processes narratives. A receiver's response to narration is influenced by the narrator's framing and appeal to values. Narratives that appeal to “protected values,” including core personal, national, or religious values, may be particularly effective at influencing receivers. Protected values resist compromise and are tied with identity, affective value, moral decision-making, and other aspects of social cognition. Here, we investigated the neural mechanisms underlying reactions to protected values in narratives. During fMRI scanning, we presented 78 American, Chinese, and Iranian participants with real-life stories distilled from a corpus of over 20 million weblogs. Reading these stories engaged the posterior medial, medial prefrontal, and temporo-parietal cortices. When participants believed that the protagonist was appealing to a protected value, signal in these regions was increased compared with when no protected value was perceived, possibly reflecting the intensive and iterative search required to process this material. The effect strength also varied across groups, potentially reflecting cultural differences in the degree of concern for protected values.
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