Search Results for author: Ethan Fast

Found 6 papers, 1 papers with code

Iris: A Conversational Agent for Complex Tasks

no code implementations17 Jul 2017 Ethan Fast, Binbin Chen, Julia Mendelsohn, Jonathan Bassen, Michael Bernstein

Today's conversational agents are restricted to simple standalone commands.

Long-Term Trends in the Public Perception of Artificial Intelligence

no code implementations16 Sep 2016 Ethan Fast, Eric Horvitz

We find that discussion of AI has increased sharply since 2009, and that these discussions have been consistently more optimistic than pessimistic.

Identifying Dogmatism in Social Media: Signals and Models

no code implementations EMNLP 2016 Ethan Fast, Eric Horvitz

When we use our predictive model to analyze millions of other Reddit posts, we find evidence that suggests dogmatism is a deeper personality trait, present for dogmatic users across many different domains, and that users who engage on dogmatic comments tend to show increases in dogmatic posts themselves.

Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community

no code implementations29 Mar 2016 Ethan Fast, Tina Vachovsky, Michael S. Bernstein

In this paper, we present a technique that combines natural language processing with a crowdsourced lexicon of stereotypes to capture gender biases in fiction.

Augur: Mining Human Behaviors from Fiction to Power Interactive Systems

no code implementations22 Feb 2016 Ethan Fast, William McGrath, Pranav Rajpurkar, Michael Bernstein

From smart homes that prepare coffee when we wake, to phones that know not to interrupt us during important conversations, our collective visions of HCI imagine a future in which computers understand a broad range of human behaviors.

Empath: Understanding Topic Signals in Large-Scale Text

2 code implementations22 Feb 2016 Ethan Fast, Binbin Chen, Michael Bernstein

Given a small set of seed words that characterize a category, Empath uses its neural embedding to discover new related terms, then validates the category with a crowd-powered filter.

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