155 research outputs found
Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review
Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that
combining deep learning with symbolic reasoning will lead to stronger AI than
either paradigm on its own. As successful as deep learning has been, it is
generally accepted that even our best deep learning systems are not very good
at abstract reasoning. And since reasoning is inextricably linked to language,
it makes intuitive sense that Natural Language Processing (NLP), would be a
particularly well-suited candidate for NeSy. We conduct a structured review of
studies implementing NeSy for NLP, with the aim of answering the question of
whether NeSy is indeed meeting its promises: reasoning, out-of-distribution
generalization, interpretability, learning and reasoning from small data, and
transferability to new domains. We examine the impact of knowledge
representation, such as rules and semantic networks, language structure and
relational structure, and whether implicit or explicit reasoning contributes to
higher promise scores. We find that systems where logic is compiled into the
neural network lead to the most NeSy goals being satisfied, while other factors
such as knowledge representation, or type of neural architecture do not exhibit
a clear correlation with goals being met. We find many discrepancies in how
reasoning is defined, specifically in relation to human level reasoning, which
impact decisions about model architectures and drive conclusions which are not
always consistent across studies. Hence we advocate for a more methodical
approach to the application of theories of human reasoning as well as the
development of appropriate benchmarks, which we hope can lead to a better
understanding of progress in the field. We make our data and code available on
github for further analysis.Comment: Surve
Research in the Language, Information and Computation Laboratory of the University of Pennsylvania
This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania.
It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition.
Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue it’s easier than ever to do so: this document is accessible on the “information superhighway”. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html
In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authors’ abstracts in the web version of this report.
The abstracts describe the researchers’ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn
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