An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models
Bibliographic record
Abstract
This paper addresses the challenges associated with machine translation of patents from English to Japanese. This translation poses unique difficulties due to their legal nature, distinguishing them from general Japanese-to-English translation. Furthermore, the complexities inherent in the Japanese language add an additional layer of intricacy to the development of effective translation models within this specific domain. Our approach encompasses a range of essential steps, including preprocessing, data preparation, expert feedback acquisition, and linguistic analysis. These steps collectively contribute to the enhancement of machine learning model performance. The experimental results, presented in this study, evaluate three prominent alternatives considered for the final step of the transformer model. Through our methodology, which incorporates a modified version of NLP-Model-III, we achieved outstanding performance for the given problem, attaining an impressive BLEU score of 46.8. Furthermore, significant improvements of up to three points on the BLEU score were observed through hyperparameter fine-tuning. This research also involved the development of a novel dataset consisting of meticulously collected patent document data. The findings of this study provide valuable insights and contribute to the advancement of Japanese patent translation methodologies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".