skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Frazier, Spencer"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; (, Proceedings of the 3rd Workshop on Narrative Understanding)
    null (Ed.)
    Accepted Manuscript Full Text Available
  2. ; ; ; (, Proceedings of the 3rd Workshop on Narrative Understanding)
    null (Ed.)
    Accepted Manuscript Full Text Available
  3. ; ; ; (, AIES '20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society)
    Value alignment is a property of an intelligent agent indicating that it can only pursue goals and activities that are beneficial to humans. Traditional approaches to value alignment use imitation learning or preference learning to infer the values of humans by observing their behavior. We introduce a complementary technique in which a value-aligned prior is learned from naturally occurring stories which encode societal norms. Training data is sourced from the children's educational comic strip, Goofus & Gallant. In this work, we train multiple machine learning models to classify natural language descriptions of situations found in the comic strip as normative or non-normative by identifying if they align with the main characters' behavior. We also report the models' performance when transferring to two unrelated tasks with little to no additional training on the new task. 
    more » « less
    Full Text Available 
  4. ; ; ; (, International Conference on Natural Language Generation)
    null (Ed.)
    Accepted Manuscript Full Text Available
  5. ; ; ; (, Proceedings of the NeurIPS Workshop on Wordplay: When Language Meets Games)
    null (Ed.)
    Accepted Manuscript Full Text Available
  6. ; ; ; (, Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society)
    null (Ed.)
    Accepted Manuscript Full Text Available