10,489 research outputs found
Computational Models (of Narrative) for Literary Studies
In the last decades a growing body of literature in Artificial Intelligence (AI) and Cognitive
Science (CS) has approached the problem of narrative understanding by means of computational
systems. Narrative, in fact, is an ubiquitous element in our everyday activity and
the ability to generate and understand stories, and their structures, is a crucial cue of our intelligence.
However, despite the fact that - from an historical standpoint - narrative (and narrative
structures) have been an important topic of investigation in both these areas, a more
comprehensive approach coupling them with narratology, digital humanities and literary
studies was still lacking.
With the aim of covering this empty space, in the last years, a multidisciplinary effort
has been made in order to create an international meeting open to computer scientist, psychologists,
digital humanists, linguists, narratologists etc.. This event has been named CMN
(for Computational Models of Narrative) and was launched in the 2009 by the MIT scholars
Mark A. Finlayson and Patrick H. Winston1
On Cognitive Preferences and the Plausibility of Rule-based Models
It is conventional wisdom in machine learning and data mining that logical
models such as rule sets are more interpretable than other models, and that
among such rule-based models, simpler models are more interpretable than more
complex ones. In this position paper, we question this latter assumption by
focusing on one particular aspect of interpretability, namely the plausibility
of models. Roughly speaking, we equate the plausibility of a model with the
likeliness that a user accepts it as an explanation for a prediction. In
particular, we argue that, all other things being equal, longer explanations
may be more convincing than shorter ones, and that the predominant bias for
shorter models, which is typically necessary for learning powerful
discriminative models, may not be suitable when it comes to user acceptance of
the learned models. To that end, we first recapitulate evidence for and against
this postulate, and then report the results of an evaluation in a
crowd-sourcing study based on about 3.000 judgments. The results do not reveal
a strong preference for simple rules, whereas we can observe a weak preference
for longer rules in some domains. We then relate these results to well-known
cognitive biases such as the conjunction fallacy, the representative heuristic,
or the recogition heuristic, and investigate their relation to rule length and
plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus
on plausibility and relation to interpretability, comprehensibility, and
justifiabilit
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