Using context-dependent interpolation to combine statistical language and translation models for interactive machine translation
Bibliographic record
Abstract
This work is in the context of TRANSTYPE, a system that watches over the user as he or she types a translation and repeatedly suggests completions for the text already entered. The user may either accept, modify, or ignore these suggestions. The system's proposals are selected and scored using a linear combination of a trigram language model and a translation model. We investigate the issue of how weights should be assigned to these two models in different contexts. 1 Introduction TRANSTYPE is part of a project set up to explore an appealing solution to the problem of using Interactive Machine Translation (IMT) as a tool for professional or other highly-skilled translators. IMT first appeared as part of Kay's MIND system (Kay, 1973), where the user's role was to help the computer analyse the source text by answering questions about word sense, ellipsis, phrasal attachments, etc. Most later work on IMT, eg (Blanchon, 1991; Brown and Nirenburg, 1990; Maruyama and Watanabe, 1990; Whitel...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".