Speech based machine aided human translation for a document translation task
Bibliographic record
Abstract
Translating documents into multiple languages represents an extremely large expensefor businesses, governments, and international agencies. In Canada, for example, it isa requirement that all ocial documents exist in both ocial languages, French andEnglish. This has produced a large translation industry employing a large number ofskilled professional translators.It is well known that the standards posed on the quality of translations for businessand government documents are far too high to apply existing automatic machinetranslation technology to the document translation task. A large number of tools forincreasing the eciency of human translators at various stages of their work ow havebecome commercially available to translation bureaus. These human translators maydirectly enter translated text, dictate their translations so they may be automaticallytranscribed, or post-edit rst draft translations produced by an automatic machinetranslation system. The work in this thesis is concerned with a machine aided humantranslation(MAHT) scenario where a human translator dictates translations ofa source language document. Automatic techniques are developed for improving thequality of the transcriptions obtained from these dictated translations by simultaneouslyincorporating knowledge from the source language text and the target languagespeech.The main contributions of this thesis are as follows. First, we describe novelalgorithms that provide ecient and accurate transcriptions of dictations providedby the human translator. We show that by integrating information extracted fromthe source language document with statistical models used in the automatic speechrecognition system, a more accurate transcription of the dictations can be obtained.Second, we use key information from the source language document like named entitytagged words and use acoustic, language and phonetic information to ensure that thatinformation exists in the translated document as well. Third, we describe a systemthat is specic to document translation. The document translation task domainaddressed here can be distinguished from tasks addressed in most previous MAHTresearch which has been focused on translating isolated sentences or phrases. Fourth,we created a new corpus, specically for use in this thesis. This corpus was collected atMcGill from professional translators dictating their translations and has been essentialfor characterizing the issues associated with the dictation-based MAHT task domain.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".