4,688 research outputs found
SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks
In this paper, we describe a so-called screening approach for learning robust
processing of spontaneously spoken language. A screening approach is a flat
analysis which uses shallow sequences of category representations for analyzing
an utterance at various syntactic, semantic and dialog levels. Rather than
using a deeply structured symbolic analysis, we use a flat connectionist
analysis. This screening approach aims at supporting speech and language
processing by using (1) data-driven learning and (2) robustness of
connectionist networks. In order to test this approach, we have developed the
SCREEN system which is based on this new robust, learned and flat analysis.
In this paper, we focus on a detailed description of SCREEN's architecture,
the flat syntactic and semantic analysis, the interaction with a speech
recognizer, and a detailed evaluation analysis of the robustness under the
influence of noisy or incomplete input. The main result of this paper is that
flat representations allow more robust processing of spontaneous spoken
language than deeply structured representations. In particular, we show how the
fault-tolerance and learning capability of connectionist networks can support a
flat analysis for providing more robust spoken-language processing within an
overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial
Intelligence Research 6(1), 199
Models of Cognition: Neurological possibility does not indicate neurological plausibility
Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Cognitive functions, like learning to speak, or discovering syntactical structures in language, have been modeled and these models are the basis for many claims about human cognitive capacities. Artificial neural nets (ANNs) have had some successes in the field of Artificial Intelligence, but the results from experiments with simple ANNs may have little value in explaining cognitive functions. The problem seems to be in relating cognitive concepts that belong in the `top-down' approach to models grounded in the `bottom-up' connectionist methodology. Merging the two fundamentally different paradigms within a single model can obfuscate what is really modeled. When the tools (simple artificial neural networks) to solve the problems (explaining aspects of higher cognitive functions) are mismatched, models with little value in terms of explaining functions of the human mind are produced. The ability to learn functions from data-points makes ANNs very attractive analytical tools. These tools can be developed into valuable models, if the data is adequate and a meaningful interpretation of the data is possible. The problem is, that with appropriate data and labels that fit the desired level of description, almost any function can be modeled. It is my argument that small networks offer a universal framework for modeling any conceivable cognitive theory, so that neurological possibility can be demonstrated easily with relatively simple models. However, a model demonstrating the possibility of implementation of a cognitive function using a distributed methodology, does not necessarily add support to any claims or assumptions that the cognitive function in question, is neurologically plausible
Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification
Connectionist temporal classification (CTC) is a powerful approach for
sequence-to-sequence learning, and has been popularly used in speech
recognition. The central ideas of CTC include adding a label "blank" during
training. With this mechanism, CTC eliminates the need of segment alignment,
and hence has been applied to various sequence-to-sequence learning problems.
In this work, we applied CTC to abstractive summarization for spoken content.
The "blank" in this case implies the corresponding input data are less
important or noisy; thus it can be ignored. This approach was shown to
outperform the existing methods in term of ROUGE scores over Chinese Gigaword
and MATBN corpora. This approach also has the nice property that the ordering
of words or characters in the input documents can be better preserved in the
generated summaries.Comment: Accepted by Interspeech 201
Having Your Cake and Eating It Too: Autonomy and Interaction in a Model of Sentence Processing
Is the human language understander a collection of modular processes
operating with relative autonomy, or is it a single integrated process? This
ongoing debate has polarized the language processing community, with two
fundamentally different types of model posited, and with each camp concluding
that the other is wrong. One camp puts forth a model with separate processors
and distinct knowledge sources to explain one body of data, and the other
proposes a model with a single processor and a homogeneous, monolithic
knowledge source to explain the other body of data. In this paper we argue that
a hybrid approach which combines a unified processor with separate knowledge
sources provides an explanation of both bodies of data, and we demonstrate the
feasibility of this approach with the computational model called COMPERE. We
believe that this approach brings the language processing community
significantly closer to offering human-like language processing systems.Comment: 7 pages, uses aaai.sty macr
A Deflationary Account of Mental Representation
Among the cognitive capacities of evolved creatures is the capacity to represent. Theories in cognitive neuroscience typically explain our manifest representational capacities by positing internal representations, but there is little agreement about how these representations function, especially with the relatively recent proliferation of connectionist, dynamical, embodied, and enactive approaches to cognition. In this talk I sketch an account of the nature and function of representation in cognitive neuroscience that couples a realist construal of representational vehicles with a pragmatic account of mental content. I call the resulting package a deflationary account of mental representation and I argue that it avoids the problems that afflict competing accounts
Usage-based and emergentist approaches to language acquisition
It was long considered to be impossible to learn grammar based on linguistic experience alone. In the past decade, however, advances in usage-based linguistic theory, computational linguistics, and developmental psychology changed the view on this matter. So-called usage-based and emergentist approaches to language acquisition state that language can be learned from language use itself, by means of social skills like joint attention, and by means of powerful generalization mechanisms. This paper first summarizes the assumptions regarding the nature of linguistic representations and processing. Usage-based theories are nonmodular and nonreductionist, i.e., they emphasize the form-function relationships, and deal with all of language, not just selected levels of representations. Furthermore, storage and processing is considered to be analytic as well as holistic, such that there is a continuum between children's unanalyzed chunks and abstract units found in adult language. In the second part, the empirical evidence is reviewed. Children's linguistic competence is shown to be limited initially, and it is demonstrated how children can generalize knowledge based on direct and indirect positive evidence. It is argued that with these general learning mechanisms, the usage-based paradigm can be extended to multilingual language situations and to language acquisition under special circumstances
Computation vs. Information Processing: Why Their Difference Matters to Cognitive Science
Since the cognitive revolution, itās become commonplace that cognition involves both computation and information processing. Is this one claim or two? Is computation the same as information processing? The two terms are often used interchangeably, but this usage masks important differences. In this paper, we distinguish information processing from computation and examine some of their mutual relations, shedding light on the role each can play in a theory of cognition. We recommend that theorists of cognition be explicit and careful in choosing\ud
notions of computation and information and connecting them together. Much confusion can be avoided by doing so
Uniform Representations for Syntax-Semantics Arbitration
Psychological investigations have led to considerable insight into the
working of the human language comprehension system. In this article, we look at
a set of principles derived from psychological findings to argue for a
particular organization of linguistic knowledge along with a particular
processing strategy and present a computational model of sentence processing
based on those principles. Many studies have shown that human sentence
comprehension is an incremental and interactive process in which semantic and
other higher-level information interacts with syntactic information to make
informed commitments as early as possible at a local ambiguity. Early
commitments may be made by using top-down guidance from knowledge of different
types, each of which must be applicable independently of others. Further
evidence from studies of error recovery and delayed decisions points toward an
arbitration mechanism for combining syntactic and semantic information in
resolving ambiguities. In order to account for all of the above, we propose
that all types of linguistic knowledge must be represented in a common form but
must be separable so that they can be applied independently of each other and
integrated at processing time by the arbitrator. We present such a uniform
representation and a computational model called COMPERE based on the
representation and the processing strategy.Comment: 7 pages, uses cogsci94.sty macr
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