Negative Language Transfer Identification in the English Writing of Chinese and Farsi Native Speakers
Bibliographic record
Abstract
Effective communication in English can facilitate educational and employment opportunities for learners of English as an additional language (EAL) who tend to employ rules from their native language while communicating in English. This results in negative language transfer (NLT) when the rules from the mother tongue do not match those of English. One way of assisting EAL learners is to identify NLT errors in their English writing as a first step in the feedback process. However, manually identifying and providing feedback on learner NLT is a difficult task that requires time and expertise. A model that automatically identifies NLT in learner writing could facilitate this process. In this study, four classification algorithms were implemented to identify NLT in EAL learner writing automatically. Two of the language modelling approaches employed to classify learner errors (n-gram and recurrent neural network) were grounded in the linguistic nature of NLT, whereas the other two classifiers were general-purpose classifiers (random forest and logistic regression). The results show that the models could identify NLT in the English writing of Chinese and Farsi native speakers. Random forest outperformed all other models, yielding average weighted F1-scores of 78.1% on the Chinese FCE dataset and 94.8% on the Farsi Lang-8 dataset. This work shows that the implemented models could be used to automatically identify NLT errors in the English writing of Chinese and Farsi native speakers. The correct identification of such errors and subsequent provisioning of appropriate feedback could facilitate language learning and improve educational and employment opportunities for EAL leaners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".