Intelligent case based machine translation system

Wang JianDe; Chen ZhaoXiong; Huang HeYan

doi:10.1007/3-540-44686-9_21

Intelligent case based machine translation system

Wang JianDe^*, Chen ZhaoXiong, Huang HeYan

^*Corresponding author for this work

Chinese Academy of Sciences

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

Interactive Hybrid Strategies Machine Translation (IHSMT) system has just been designed to solve the translation problems. It forms a nice interdependent cooperation relation between human and machine by interaction. The system achieves hybrid strategy translation by synthesizing the rule-based reasoning and case-based reasoning, and therefore overcomes the demerits of single strategy. This paper has done some work on learning mechanism of this system and proposes a learning model of human-machine tracking and memorizing (HMTM). This model can store the information of human-machine interaction into memory base as case of machine learning, and then gradually accumulate knowledge to improve the intelligence of MT system.

Original language	English
Pages (from-to)	197-205
Number of pages	9
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2004
DOIs	https://doi.org/10.1007/3-540-44686-9_21
Publication status	Published - 2001
Externally published	Yes

Access to Document

10.1007/3-540-44686-9_21

Cite this

@article{ec70a0bdd5a7447f81226a5363b543c8,

title = "Intelligent case based machine translation system",

abstract = "Interactive Hybrid Strategies Machine Translation (IHSMT) system has just been designed to solve the translation problems. It forms a nice interdependent cooperation relation between human and machine by interaction. The system achieves hybrid strategy translation by synthesizing the rule-based reasoning and case-based reasoning, and therefore overcomes the demerits of single strategy. This paper has done some work on learning mechanism of this system and proposes a learning model of human-machine tracking and memorizing (HMTM). This model can store the information of human-machine interaction into memory base as case of machine learning, and then gradually accumulate knowledge to improve the intelligence of MT system.",

author = "Wang JianDe and Chen ZhaoXiong and Huang HeYan",

year = "2001",

doi = "10.1007/3-540-44686-9_21",

language = "English",

volume = "2004",

pages = "197--205",

journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

issn = "0302-9743",

publisher = "Springer Science and Business Media Deutschland GmbH",

}

TY - JOUR

T1 - Intelligent case based machine translation system

AU - JianDe, Wang

AU - ZhaoXiong, Chen

AU - HeYan, Huang

PY - 2001

Y1 - 2001

N2 - Interactive Hybrid Strategies Machine Translation (IHSMT) system has just been designed to solve the translation problems. It forms a nice interdependent cooperation relation between human and machine by interaction. The system achieves hybrid strategy translation by synthesizing the rule-based reasoning and case-based reasoning, and therefore overcomes the demerits of single strategy. This paper has done some work on learning mechanism of this system and proposes a learning model of human-machine tracking and memorizing (HMTM). This model can store the information of human-machine interaction into memory base as case of machine learning, and then gradually accumulate knowledge to improve the intelligence of MT system.

AB - Interactive Hybrid Strategies Machine Translation (IHSMT) system has just been designed to solve the translation problems. It forms a nice interdependent cooperation relation between human and machine by interaction. The system achieves hybrid strategy translation by synthesizing the rule-based reasoning and case-based reasoning, and therefore overcomes the demerits of single strategy. This paper has done some work on learning mechanism of this system and proposes a learning model of human-machine tracking and memorizing (HMTM). This model can store the information of human-machine interaction into memory base as case of machine learning, and then gradually accumulate knowledge to improve the intelligence of MT system.

UR - http://www.scopus.com/inward/record.url?scp=84947731844&partnerID=8YFLogxK

U2 - 10.1007/3-540-44686-9_21

DO - 10.1007/3-540-44686-9_21

M3 - Article

AN - SCOPUS:84947731844

SN - 0302-9743

VL - 2004

SP - 197

EP - 205

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

Intelligent case based machine translation system

Abstract

Access to Document

Other files and links

Fingerprint

Cite this