Grammar transfer in a second order recurrent neural network

Michiro Negishi; Stephen Jose Hanson

Grammar transfer in a second order recurrent neural network

Michiro Negishi, Stephen Jose Hanson

Rutgers Newark, Psychology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

It has been known that people, after being exposed to sentences generated by an artificial grammar, acquire implicit grammatical knowledge and are able to transfer the knowledge to inputs that are generated by a modified grammar. We show that a second order recurrent neural network is able to transfer grammatical knowledge from one language (generated by a Finite State Machine) to another language which differ both in vocabularies and syntax. Representation of the grammatical knowledge in the network is analyzed using linear discriminant analysis.

Original language	English (US)
Title of host publication	Advances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001
Publisher	Neural information processing systems foundation
ISBN (Print)	0262042088, 9780262042086
State	Published - 2002
Event	15th Annual Neural Information Processing Systems Conference, NIPS 2001 - Vancouver, BC, Canada Duration: Dec 3 2001 → Dec 8 2001

Publication series

Name	Advances in Neural Information Processing Systems
ISSN (Print)	1049-5258

Other

Other	15th Annual Neural Information Processing Systems Conference, NIPS 2001
Country/Territory	Canada
City	Vancouver, BC
Period	12/3/01 → 12/8/01

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

@inproceedings{bef9c150aba04ca0b139f574d2b60fda,

title = "Grammar transfer in a second order recurrent neural network",

abstract = "It has been known that people, after being exposed to sentences generated by an artificial grammar, acquire implicit grammatical knowledge and are able to transfer the knowledge to inputs that are generated by a modified grammar. We show that a second order recurrent neural network is able to transfer grammatical knowledge from one language (generated by a Finite State Machine) to another language which differ both in vocabularies and syntax. Representation of the grammatical knowledge in the network is analyzed using linear discriminant analysis.",

author = "Michiro Negishi and Hanson, {Stephen Jose}",

year = "2002",

language = "English (US)",

isbn = "0262042088",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural information processing systems foundation",

booktitle = "Advances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001",

note = "15th Annual Neural Information Processing Systems Conference, NIPS 2001 ; Conference date: 03-12-2001 Through 08-12-2001",

}

Negishi, M & Hanson, SJ 2002, Grammar transfer in a second order recurrent neural network. in Advances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001. Advances in Neural Information Processing Systems, Neural information processing systems foundation, 15th Annual Neural Information Processing Systems Conference, NIPS 2001, Vancouver, BC, Canada, 12/3/01.

Grammar transfer in a second order recurrent neural network. / Negishi, Michiro; Hanson, Stephen Jose.
Advances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001. Neural information processing systems foundation, 2002. (Advances in Neural Information Processing Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Grammar transfer in a second order recurrent neural network

AU - Negishi, Michiro

AU - Hanson, Stephen Jose

PY - 2002

Y1 - 2002

N2 - It has been known that people, after being exposed to sentences generated by an artificial grammar, acquire implicit grammatical knowledge and are able to transfer the knowledge to inputs that are generated by a modified grammar. We show that a second order recurrent neural network is able to transfer grammatical knowledge from one language (generated by a Finite State Machine) to another language which differ both in vocabularies and syntax. Representation of the grammatical knowledge in the network is analyzed using linear discriminant analysis.

AB - It has been known that people, after being exposed to sentences generated by an artificial grammar, acquire implicit grammatical knowledge and are able to transfer the knowledge to inputs that are generated by a modified grammar. We show that a second order recurrent neural network is able to transfer grammatical knowledge from one language (generated by a Finite State Machine) to another language which differ both in vocabularies and syntax. Representation of the grammatical knowledge in the network is analyzed using linear discriminant analysis.

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M3 - Conference contribution

AN - SCOPUS:84899026336

SN - 0262042088

SN - 9780262042086

T3 - Advances in Neural Information Processing Systems

BT - Advances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001

PB - Neural information processing systems foundation

T2 - 15th Annual Neural Information Processing Systems Conference, NIPS 2001

Y2 - 3 December 2001 through 8 December 2001

ER -

Grammar transfer in a second order recurrent neural network

Abstract

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Other

All Science Journal Classification (ASJC) codes

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