194,030 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
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
Reinventing College Physics for Biologists: Explicating an epistemological curriculum
The University of Maryland Physics Education Research Group (UMd-PERG)
carried out a five-year research project to rethink, observe, and reform
introductory algebra-based (college) physics. This class is one of the Maryland
Physics Department's large service courses, serving primarily life-science
majors. After consultation with biologists, we re-focused the class on helping
the students learn to think scientifically -- to build coherence, think in
terms of mechanism, and to follow the implications of assumptions. We designed
the course to tap into students' productive conceptual and epistemological
resources, based on a theoretical framework from research on learning. The
reformed class retains its traditional structure in terms of time and
instructional personnel, but we modified existing best-practices curricular
materials, including Peer Instruction, Interactive Lecture Demonstrations, and
Tutorials. We provided class-controlled spaces for student collaboration, which
allowed us to observe and record students learning directly. We also scanned
all written homework and examinations, and we administered pre-post conceptual
and epistemological surveys. The reformed class enhanced the strong gains on
pre-post conceptual tests produced by the best-practices materials while
obtaining unprecedented pre-post gains on epistemological surveys instead of
the traditional losses.Comment: 35 pages including a 15 page appendix of supplementary material
The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments
In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge.
We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and
future challenges
Hearing meanings: the revenge of context
According to the perceptual view of language comprehension, listeners typically recover high-level linguistic properties such as utterance meaning without inferential work. The perceptual view is subject to the Objection from Context: since utterance meaning is massively context-sensitive, and context-sensitivity requires cognitive inference, the perceptual view is false. In recent work, Berit Brogaard provides a challenging reply to this objection. She argues that in language comprehension context-sensitivity is typically exercised not through inferences, but rather through top-down perceptual modulations or perceptual learning. This paper provides a complete formulation of the Objection from Context and evaluates Brogaards reply to it. Drawing on conceptual considerations and empirical examples, we argue that the exercise of context-sensitivity in language comprehension does, in fact, typically involve inference
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Understanding analogical reasoning : viewpoints from psychology and related disciplines
Analogy and metaphor have a long history of study in linguistics, education, philosophy and psychology. Consensus over what analogy is or how analogy functions in language and thought, however, has been elusive. This paper, the first in a two part series, examines these various research traditions, attempting to bring out major lines of agreement over the role of analogy in individual human experience. As well as being a general literature review which may be helpful for newcomers to the study of analogy, this paper attempts to extract from these literatures existing theories, models and concepts which may be interesting or useful for computational studies of analogical reasoning
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