Augmenting Performance of SMT Models by Deploying Fine Tokenization of the Text and Part-of-Speech Tag
Bibliographic record
Abstract
This paper presents our study of exploiting the languages’ word class information augmented with some rule-based processing for phrase-based Statistical Machine Translation (SMT). In statistical machine translation, estimating word-to-word alignment probabilities for the translation model can be difficult due to the problem of sparse data: most words in a given corpus occur at most a handful of times. With a highly inflected language such as Oromo, this problem can be particularly severe. In addition, there is variant nature or use of different symbols for ‘hudhaa’ (the diacritical marker) in Oromo language which intrudes another severe data sparsity problem. In this work, we show that using fine tokenization of words considering intra-word behavior of words consisting hudhaa, and POS tag to modify the Oromo input and see how it improves Oromo-English machine translation system. The models were trained on a very small parallel corpus of data set (usually unacceptable for normal SMT system) and also the quality of the parallel corpus both in translation and spelling errors were not so good. Yet, our final system achieves a BLEU score of 2.88, as compared to 2.56 for the baseline system.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".