Design of Intelligent Response System for Business English (Training) Based on Improved GLR Algorithm

Liu, Ying

doi:10.1007/978-3-031-05237-8_2

Cited by 2 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These conflicts occur when multiple parsing actions are possible at a particular parsing state, making it challenging to determine the correct course of action. [17][18][19] Advanced conflict resolution mechanisms aim to address these parsing conflicts in a more sophisticated and informed manner, improving the accuracy and efficiency of the parsing process. This TF-IDF feature extraction process results in numerical vectors that effectively capture word importance, serving as meaningful input features for subsequent machine learning or statistical models.…”

Section: F Glr Parsing Enhancementsmentioning

confidence: 99%

Research on Automatic Identification of Machine English Translation Errors Based on Improved GLR Algorithm

2024

IJCAI

View full text Add to dashboard Cite

Machine translation is a powerful tool for overcoming linguistic obstacles, but it often introduces errors that lower the overall translation quality. This research project aims to enhance machine-translated documents by identifying and classifying translation faults. To identify errors, the traditional Generalized LR (GLR) technique is modified and enhanced, incorporating linguistic and statistical elements from the machine-translated texts. Contextual information from GLR parsing is utilized to improve error detection, and additional parsing algorithms are integrated to handle the complexities of machine translation. The proposed improved GLR algorithm is compared with three baseline models: the statistical algorithm, dynamic memory algorithm, and traditional GLR algorithm. The evaluation is based on two key metrics: accuracy and recognition speed, with a focus on renewal capability. The improved GLR algorithm achieves a significantly higher accuracy of 92.5% compared to the baseline models: statistical algorithm (85.2%), dynamic memory algorithm (88.9%), and traditional GLR algorithm (80.6%). Additionally, the improved GLR algorithm demonstrates a recognition speed of 1200 words per second, showcasing its efficiency in real-time translation scenarios. The results show that the enhanced GLR algorithm outperforms the baseline models in accurately detecting translation errors while maintaining an efficient recognition speed. Its high renewal capability ensures adaptability to changing translation challenges and continuous improvement over time.Povzetek: Raziskava izboljša avtomatsko identifikacijo napak v strojnem prevajanju z nadgrajenim GLR algoritmom, dosegajoč 92.5% natančnost in hitrost 1200 besed na sekundo.

show abstract

Section: F Glr Parsing Enhancementsmentioning

confidence: 99%

Research on Automatic Identification of Machine English Translation Errors Based on Improved GLR Algorithm

2024

IJCAI

View full text Add to dashboard Cite

show abstract

Design of English Translation Model for Intelligent Recognition of Fu Tea Culture Based on Improved GLR Algorithm

Wang,

Yu,

Sui

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Design of Intelligent Response System for Business English (Training) Based on Improved GLR Algorithm

Cited by 2 publications

References 9 publications

Research on Automatic Identification of Machine English Translation Errors Based on Improved GLR Algorithm

Research on Automatic Identification of Machine English Translation Errors Based on Improved GLR Algorithm

Design of English Translation Model for Intelligent Recognition of Fu Tea Culture Based on Improved GLR Algorithm

Contact Info

Product

Resources

About