Learning consistent semantics from training data
Bibliographic record
Abstract
Previously (see ICASSP-93, vol.2, p.55, 1993 and Eurospeech 93, vol.2, p.1331, 1993) we described a speech understanding system called "CHANEL" with two components: a chart-based parser that analyzes semantically important word islands within an utterance; and a component based on "semantic classification trees" (SCTs) that builds the representation for the complete utterance. The construction of a natural-language understanding (NLU) system is a task that has traditionally required lavish expenditure of programmer-hours. By dividing the task in this way, we enabled many of the system's rules (those contained in the SCT component) to be learned automatically from training data, freeing human expertise to be applied where it is most effective. This paper describes recent improvements to both components of CHANEL, along with a new module that handles context-dependent utterances. The new version of CHANEL has a new use for SCTs: a special SCT decides whether a sentence is context-dependent or not.>
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".