Linguistic-Relationships-Based Approach for Improving Word Alignment
Why this work is in the frame
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Bibliographic record
Abstract
The unsupervised word alignments (such as GIZA++) are widely used in the phrase-based statistical machine translation. The quality of the model is proportional to the size and the quality of the bilingual corpus. However, for low-resource language pairs such as Chinese and Vietnamese, a result of unsupervised word alignment sometimes is of low quality due to the sparse data. In addition, this model does not take advantage of the linguistic relationships to improve performance of word alignment. Chinese and Vietnamese have the same language type and have close linguistic relationships. In this article, we integrate the characteristics of linguistic relationships into the word alignment model to enhance the quality of Chinese-Vietnamese word alignment. These linguistic relationships are Sino-Vietnamese and content word. The experimental results showed that our method improved the performance of word alignment as well as the quality of machine translation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it