Enhancing Machine Translation for English-Japanese Using Syntactic Pattern Recognition Methods
Bibliographic record
Abstract
In this thesis, we present a novel approach to machine translation using syntactic Pattern Recognition (PR) methods.The purpose of this research is to evaluate the possibility of using syntactic PR techniques in this field, as well as to identify any potential benefits in such an approach.To make use of syntactic PR techniques, we propose a system that performs string-matching to pair English sentence structures to Japanese structures, with the goal of facilitating translation between the languages.In order to process the sentence structures of either language as a string, we have created a representation that replaces the tokens of a sentence with their respective Part-of-Speech tags, using a hybrid tag set created for this system.To perform the actual string-matching operation we make use of the OptPR algorithm, a syntactic award-winning PR scheme that has been proven to achieve optimal accuracy.Through our experiments, we show that our implementation obtains superior results to that of a standard statistical machine translation system on our data set, with the additional guarantee of generating a known sentence structure in the target language.With further research, this system could be expanded to have a more complete coverage of the languages worked with, given the capability to handle more complex sentence structures.iii
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".