3 research outputs found
Studying Dishonest Intentions in Brazilian Portuguese Texts
Previous work in the social sciences, psychology and linguistics has show
that liars have some control over the content of their stories, however their
underlying state of mind may "leak out" through the way that they tell them. To
the best of our knowledge, no previous systematic effort exists in order to
describe and model deception language for Brazilian Portuguese. To fill this
important gap, we carry out an initial empirical linguistic study on false
statements in Brazilian news. We methodically analyze linguistic features using
a deceptive news corpus, which includes both fake and true news. The results
show that they present substantial lexical, syntactic and semantic variations,
as well as punctuation and emotion distinctions
Adversarially Guided Self-Play for Adopting Social Conventions
Robotic agents must adopt existing social conventions in order to be
effective teammates. These social conventions, such as driving on the right or
left side of the road, are arbitrary choices among optimal policies, but all
agents on a successful team must use the same convention. Prior work has
identified a method of combining self-play with paired input-output data
gathered from existing agents in order to learn their social convention without
interacting with them. We build upon this work by introducing a technique
called Adversarial Self-Play (ASP) that uses adversarial training to shape the
space of possible learned policies and substantially improves learning
efficiency. ASP only requires the addition of unpaired data: a dataset of
outputs produced by the social convention without associated inputs.
Theoretical analysis reveals how ASP shapes the policy space and the
circumstances (when behaviors are clustered or exhibit some other structure)
under which it offers the greatest benefits. Empirical results across three
domains confirm ASP's advantages: it produces models that more closely match
the desired social convention when given as few as two paired datapoints.Comment: 9 pages, 8 figure
Weakest Preexpectation Semantics for Bayesian Inference
We present a semantics of a probabilistic while-language with soft
conditioning and continuous distributions which handles programs diverging with
positive probability. To this end, we extend the probabilistic guarded command
language (pGCL) with draws from continuous distributions and a score operator.
The main contribution is an extension of the standard weakest preexpectation
semantics to support these constructs. As a sanity check of our semantics, we
define an alternative trace-based semantics of the language, and show that the
two semantics are equivalent. Various examples illustrate the applicability of
the semantics