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Automatic Scoring for Translations Based on Language Models.

Diming Wu¹, Mingke Wang², Xiaomin Li²

¹School of Foreign Language, Bengbu University, Bengbu, China.

Computational Intelligence and Neuroscience

|July 8, 2022

Summary

This summary is machine-generated.

Automated translation scoring using dialogue system metrics is feasible. This approach offers an objective alternative to manual grading, aligning closely with human assessments for improved efficiency in English language education.

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Area of Science:

Natural Language Processing
Educational Technology
Computational Linguistics

Background:

Manual translation scoring is time-consuming and subjective.
Developing objective and efficient automated scoring methods is crucial for English education.
Dialogue system evaluation metrics share similarities with translation quality assessment.

Purpose of the Study:

To investigate the feasibility of applying dialogue system evaluation metrics to automatic translation scoring.
To assess the objectivity and efficiency of automated translation scoring compared to manual methods.

Main Methods:

Selected two dialogue system metrics for automatic translation scoring.
Conducted a case study applying these metrics to student translations.
Compared the automated scores with human scoring benchmarks.

Main Results:

The hybrid scores generated by the two dialogue metrics closely approximated human scores.
The experimental results demonstrate the viability of the proposed automated scoring approach.

Conclusions:

Applying dialogue system evaluation metrics to translation scoring is a feasible method.
This approach offers a promising avenue for improving the objectivity and efficiency of automated translation scoring in educational settings.