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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Comparing product quality between translation and paraphrasing: Using NLP-assisted evaluation frameworks.

Tianyi Han1, Dechao Li1, Xingcheng Ma2

  • 1Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.

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|December 12, 2022
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Summary
This summary is machine-generated.

This study developed NLP-assisted frameworks to evaluate translation and paraphrasing quality, finding strong correlations between the two tasks, suggesting shared language skills and informing pedagogical applications in second language acquisition.

Keywords:
linguistic featuresnatural language processingparaphrasing quality assessmentproduct qualitytranslation quality assessment

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Area of Science:

  • Second Language Acquisition (SLA)
  • Natural Language Processing (NLP)
  • Computational Linguistics

Background:

  • Translation and paraphrasing are key L2 communication forms and learning methods.
  • Existing quality assessment methods for these tasks are often subjective or inconsistent.
  • Lack of research on the correlation in product quality between translation and paraphrasing.

Purpose of the Study:

  • To propose and validate NLP-assisted evaluation frameworks for translation and paraphrasing quality.
  • To compare the product quality of translation and paraphrasing activities.
  • To investigate the correlation between translation and paraphrasing performance.

Main Methods:

  • Developed NLP-assisted evaluation frameworks integrating NLP indices with expert rubrics.
  • Recruited 29 translators for Chinese-to-English translation and English paraphrasing tasks.
  • Collected data using key-logging and graded outputs using Likert scales and NLP tools (TAASSC, TAALES).

Main Results:

  • Identified key NLP indices predicting product quality (lexical, syntactic features).
  • Found strong positive correlations between translated and paraphrased text quality.
  • Observed shared predictive indices in lexical sophistication, indicating common underlying competencies.

Conclusions:

  • Translation and paraphrasing share underlying language competence and mental strategies.
  • Differences in salient textual features are linked to task-specific processing costs.
  • The NLP-assisted framework offers insights for standardized evaluation and pedagogical applications in SLA.