Difference between revisions of "Native Language and Dialect"
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* Models providing automated speech scores on English language proficiency assessment | * Models providing automated speech scores on English language proficiency assessment | ||
* L1-specific model trained on the speaker’s native language was the least fair, especially for Chinese, Japanese, and Korean speakers, but not for German speakers | * L1-specific model trained on the speaker’s native language was the least fair, especially for Chinese, Japanese, and Korean speakers, but not for German speakers | ||
* All models (Baseline, Fair feature subset, L1-specific) performed | * All models (Baseline, Fair feature subset, L1-specific) performed worse for Japanese speakers | ||
Revision as of 19:41, 17 May 2022
Naismith et al. (2018) pdf
- a model that measures L2 learners’ lexical sophistication with the frequency list based on the native speaker corpora
- Arabic-speaking learners are rated systematically lower across all levels of human-assessed English proficiency than speakers of Chinese, Japanese, Korean, and Spanish.
- Level 5 Arabic-speaking learners are inaccurately evaluated to have similar level of lexical sophistication as Level 4 learners from China, Japan, Korean and Spain .
- When used on the ETS corpus, essays by Japanese-speaking learners with higher human-rated lexical sophistication are rated significantly lower in lexical sophistication than Arabic, Japanese, Korean and Spanish peers.
Loukina et al. (2019) pdf
- Models providing automated speech scores on English language proficiency assessment
- L1-specific model trained on the speaker’s native language was the least fair, especially for Chinese, Japanese, and Korean speakers, but not for German speakers
- All models (Baseline, Fair feature subset, L1-specific) performed worse for Japanese speakers