63,901 research outputs found
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
Reasoning is a crucial part of natural language argumentation. To comprehend
an argument, one must analyze its warrant, which explains why its claim follows
from its premises. As arguments are highly contextualized, warrants are usually
presupposed and left implicit. Thus, the comprehension does not only require
language understanding and logic skills, but also depends on common sense. In
this paper we develop a methodology for reconstructing warrants systematically.
We operationalize it in a scalable crowdsourcing process, resulting in a freely
licensed dataset with warrants for 2k authentic arguments from news comments.
On this basis, we present a new challenging task, the argument reasoning
comprehension task. Given an argument with a claim and a premise, the goal is
to choose the correct implicit warrant from two options. Both warrants are
plausible and lexically close, but lead to contradicting claims. A solution to
this task will define a substantial step towards automatic warrant
reconstruction. However, experiments with several neural attention and language
models reveal that current approaches do not suffice.Comment: Accepted as NAACL 2018 Long Paper; see details on the front pag
The Narrow Conception of Computational Psychology
One particularly successful approach to modeling within cognitive science is computational psychology. Computational psychology explores psychological processes by building and testing computational models with human data. In this paper, it is argued that a specific approach to understanding computation, what is called the ‘narrow conception’, has problematically limited the kinds of models, theories, and explanations that are offered within computational psychology. After raising two problems for the narrow conception, an alternative, ‘wide approach’ to computational psychology is proposed
Higher-level Knowledge, Rational and Social Levels Constraints of the Common Model of the Mind
In his famous 1982 paper, Allen Newell [22, 23] introduced the notion of knowledge level to
indicate a level of analysis, and prediction, of the rational behavior of a cognitive articial agent.
This analysis concerns the investigation about the availability of the agent knowledge, in order
to pursue its own goals, and is based on the so-called Rationality Principle (an assumption
according to which "an agent will use the knowledge it has of its environment to achieve its
goals" [22, p. 17]. By using the Newell's own words: "To treat a system at the knowledge level
is to treat it as having some knowledge, some goals, and believing it will do whatever is within
its power to attain its goals, in so far as its knowledge indicates" [22, p. 13].
In the last decades, the importance of the knowledge level has been historically and system-
atically downsized by the research area in cognitive architectures (CAs), whose interests have
been mainly focused on the analysis and the development of mechanisms and the processes
governing human and (articial) cognition. The knowledge level in CAs, however, represents
a crucial level of analysis for the development of such articial general systems and therefore
deserves greater research attention [17]. In the following, we will discuss areas of broad agree-
ment and outline the main problematic aspects that should be faced within a Common Model
of Cognition [12]. Such aspects, departing from an analysis at the knowledge level, also clearly
impact both lower (e.g. representational) and higher (e.g. social) levels
Cognitive context and arguments from ontologies for learning
The deployment of learning resources on the web by different experts has resulted in the accessibility of multiple viewpoints about the same topics. In
this work we assume that learning resources are underpinned by ontologies. Different formalizations of domains may result from different contexts, different use of
terminology, incomplete knowledge or conflicting knowledge. We define the notion of cognitive learning context which describes the cognitive context of an agent who refers to multiple and possibly inconsistent ontologies to determine the truth of a proposition. In particular we describe the cognitive states of ambiguity and inconsistency
resulting from incomplete and conflicting ontologies respectively. Conflicts between ontologies can be identified through the derivation of conflicting arguments
about a particular point of view. Arguments can be used to detect inconsistencies between ontologies. They can also be used in a dialogue between a human learner and a software tutor in order to enable the learner to justify her views and detect inconsistencies between her beliefs and the tutor’s own. Two types of arguments are discussed, namely: arguments inferred directly from taxonomic relations
between concepts, and arguments about the necessary an
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