Elisa Kreiss    




PhD candidate in Linguistics
Stanford University
Focus on Linguistics, Computer Science, and Psychology

Publications


Modeling subjective assessments of guilt in newspaper crime narratives
Kreiss, E.*, Wang, Z.*, and Potts, C.
Submitted

[Paper] [Code & Corpus] [Bibtex]
Production expectations modulate contrastive inference
Kreiss, E., and Degen, J. (2020)
Proceedings of the 42nd Annual Conference of the Cognitive Science Society

[Paper] [Code] [Bibtex] [Explore the Data!] [Talk]
When redundancy is useful:
A Bayesian approach to "overinformative" referring expressions

Degen, J., Hawkins, R.D., Graf, C., Kreiss, E., and Goodman, N.D. (2020)
Psychological Review

[Paper] [Code] [Bibtex]
Uncertain evidence statements and guilt perception in iterative reproductions of crime stories
Kreiss, E., Franke, M., Degen, J. (2019)
Proceedings of the 41st Annual Conference of the Cognitive Science Society

[Paper] [Code & Corpus] [Bibtex]
Modeling Natural Language in the RSA Framework:
Typicality Effects in Overinformative Referring Expressions

Kreiss, E. (2017)
Unpublished Bachelor's Thesis.

Resources


The SuspectGuilt Corpus

We are excited to share the SuspectGuilt Corpus: a collection of 474k local news crime reports from across the US. 1821 of these stories were selected for further annotation. Annotators rated (1) how likely they considered the suspect(s) of a story to be guilty (reader perception), and (2) how much the author of the report believes that the suspect(s) are guilty (author belief). For each question, annotators also highlighted why they gave their response.
This corpus presents an extensive collection of news articles with rich guilt rating and highlighting annotations, as well as the annotators' self-reported age, gender, and native language information.
With its publication, we would like to encourage more research in the domain of crime reporting and specifically guilt perception.
For more details, have a look at our corpus documentation. In Kreiss & Wang et al. (2020), we published a more detailed description of the data collection process, and show the value of rich annotations for understanding and modeling guilt perception.



The Annotated Iterated Narration Corpus (AINC)

In this corpus we have collected reproductions of news stories from participants who read carefully designed crime reports about a committed crime and the arrest of a suspect. The original stories were manipulated as to how strong the evidence seemed to be. The reproductions were then again read and reproduced by other participants. Afterwards those stories were annotated probing the reader's belief about the suspect's guilt, but also other measures, such as their emotional affectedness for each reproduction.
For more details, have a look at our corpus documentation or consult Kreiss et al. (2019) where we describe the data collection process and present some first interesting analyses.

Talks and Posters


Kreiss, E., Degen, J. Production expectations modulate contrastive inference. CogSci 2020, Toronto/hosted virtually, Jul 29-Aug 1. (Talk)

Kreiss, E., Degen, J. Production expectations modulate contrastive inference. CUNY 2020, Amherst/hosted virtually, Mar 19-21. (Poster)

Kreiss, E., Degen, J. Production expectations modulate contrastive inferences. CAMP 2019, Santa Cruz, Oct 26-27. (Talk)

Kreiss, E., Franke, M., Degen, J. Uncertain evidence statements and guilt perception in iterative reproductions of crime stories. CogSci 2019, Montreal, Jul 24-27. (Poster)

Kreiss, E., Degen, J., Hawkins, R.X.D., and Goodman, N.D. Mentioning atypical properties of objects is communicatively efficient. CogSci 2017, London, Jul 26-29. (Poster)

Kreiss, E., Degen, J., Hawkins, R.X.D., and Goodman, N.D. Mentioning atypical properties of objects is communicatively efficient. XPRAG 2017, Cologne, Jun 21-23. (Poster)

Degen, J., Kreiss, E., Hawkins, R.X.D., and Goodman, N.D. Mentioning atypical properties of objects is communicatively efficient. CUNY 2017, MIT, Mar 30-Apr 1. (Talk, presented by Judith Degen)