Xiaolei Lu
Biographic Data
| ID | 4157497 |
|---|---|
| NAME | Xiaolei Lu |
| GIVEN NAMES | Xiaolei |
| FAMILY NAME | Lu |
| SIGNATURE | LU X |
| AFFILIATIONS | Xiamen University |
| ORCID | 0000-0002-6929-4110 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 2 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
ChatGPT as an evaluator in summative assessment of interpreting
Translation and interpreting (T&I) have long been taught and assessed in higher education contexts worldwide. With the growing demand for grading student-generated T&I, researchers are focusing on automatic T&I assessment. In this article, we aim to initially investigate the utility of ChatGPT as an automatic evaluator for interpreting, by comparing ChatGPT-based scores with those of human raters. We recruited 45 raters from diverse backgrounds t…
Large language models as zero-shot evaluators of English–Chinese interpreting
Assessing translation and interpreting (T&I) is essential in tertiary-level T&I education, professional certification, and foreign language testing. Recently, researchers have explored automating T&I assessment, with large language models (LLMs) emerging as a promising agent for automatic scoring. This study presents one of the first large-scale empirical investigations into the scoring reliability, severity, and validity of GPT-4o and DeepSeek-R…
Applying n -gram-based evaluation metrics to assess human interpreting
We have recently witnessed a number of studies conducted to employ n -gram-based machine-translation evaluation metrics such as BLEU to assess human interpreting automatically. A major limitation of this research lies in the non-probabilistic sampling of a limited number of renditions. Consequently, the correlation coefficients calculated between machine and human assessments, which serve as a proxy for machine– human parity, lack generalizabilit…
Use of statistical methods in translation and interpreting research
The study reported on in the article examines the patterns and trends of statistical analysis in translation and interpreting (T&I) research, based on a longitudinal quantitative analysis of more than 3300 research articles sampled from eleven leading T&I journals (2000–2020). This evidence-based review is the first study to provide a systematic mapping of statistical methods used by T&I researchers. Our analyses suggest that (a) about 40% of the…
Automatic assessment of spoken-language interpreting based on machine-translation evaluation metrics
Automated metrics for machine translation (MT) such as BLEU are customarily used because they are quick to compute and sufficiently valid to be useful in MT assessment. Whereas the instantaneity and reliability of such metrics are made possible by automatic computation based on predetermined algorithms, their validity is primarily dependent on a strong correlation with human assessments. Despite the popularity of such metrics in MT, little resear…
Interpreting quality assessment re-imagined
Assessment of interpreting quality is a ubiquitous social practice in the interpreting industry and academia. In this article, we focus on both psychometric and social dimensions of assessment practice, and analyse two major assessment paradigms, namely, human rater scoring and automatic machine scoring. Regarding human scoring, we describe five specific methods, including atomistic scoring, questionnaire-based scoring, multi-methods scoring, rub…
Automatic assessment of spoken-language interpreting based on machine-translation evaluation metrics
Automated metrics for machine translation (MT) such as BLEU are customarily used because they are quick to compute and sufficiently valid to be useful in MT assessment. Whereas the instantaneity and reliability of such metrics are made possible by automatic computation based on predetermined algorithms, their validity is primarily dependent on a strong correlation with human assessments. Despite the popularity of such metrics in MT, little resear…
Interpreting quality assessment re-imagined
Assessment of interpreting quality is a ubiquitous social practice in the interpreting industry and academia. In this article, we focus on both psychometric and social dimensions of assessment practice, and analyse two major assessment paradigms, namely, human rater scoring and automatic machine scoring. Regarding human scoring, we describe five specific methods, including atomistic scoring, questionnaire-based scoring, multi-methods scoring, rub…
Automatic assessment of spoken-language interpreting based on machine-translation evaluation metrics
Automated metrics for machine translation (MT) such as BLEU are customarily used because they are quick to compute and sufficiently valid to be useful in MT assessment. Whereas the instantaneity and reliability of such metrics are made possible by automatic computation based on predetermined algorithms, their validity is primarily dependent on a strong correlation with human assessments. Despite the popularity of such metrics in MT, little resear…
Use of statistical methods in translation and interpreting research
The study reported on in the article examines the patterns and trends of statistical analysis in translation and interpreting (T&I) research, based on a longitudinal quantitative analysis of more than 3300 research articles sampled from eleven leading T&I journals (2000–2020). This evidence-based review is the first study to provide a systematic mapping of statistical methods used by T&I researchers. Our analyses suggest that (a) about 40% of the…
Large language models as zero-shot evaluators of English–Chinese interpreting
Assessing translation and interpreting (T&I) is essential in tertiary-level T&I education, professional certification, and foreign language testing. Recently, researchers have explored automating T&I assessment, with large language models (LLMs) emerging as a promising agent for automatic scoring. This study presents one of the first large-scale empirical investigations into the scoring reliability, severity, and validity of GPT-4o and DeepSeek-R…
Applying n -gram-based evaluation metrics to assess human interpreting
We have recently witnessed a number of studies conducted to employ n -gram-based machine-translation evaluation metrics such as BLEU to assess human interpreting automatically. A major limitation of this research lies in the non-probabilistic sampling of a limited number of renditions. Consequently, the correlation coefficients calculated between machine and human assessments, which serve as a proxy for machine– human parity, lack generalizabilit…
ChatGPT as an evaluator in summative assessment of interpreting
Translation and interpreting (T&I) have long been taught and assessed in higher education contexts worldwide. With the growing demand for grading student-generated T&I, researchers are focusing on automatic T&I assessment. In this article, we aim to initially investigate the utility of ChatGPT as an automatic evaluator for interpreting, by comparing ChatGPT-based scores with those of human raters. We recruited 45 raters from diverse backgrounds t…
Interpreting and Communication in Healthcare (5 works) · Computer Science (3 works) · Psychology (3 works) · Artificial Intelligence (2 works) · Artificial Intelligence in Healthcare and Education (2 works) · Data science (2 works) · Interpreter (2 works) · Machine learning (2 works) · Natural Language Processing Techniques (2 works) · Translation Studies and Practices (2 works)