Design and Evaluation of a Context Aware AAC Application for Pre-literate Children with Complex Communication Needs
Bibliographic record
Abstract
Nonverbal, pre-literate children must rely on communication devices to interact with others, but contemporary communication aids are inefficient as they ignore conversational context. This thesis presents the design and implementation of a conversational context-aware augmentative and alternative communication (AAC) device. A query from an AAC user’s communication partner serves as the input to our system. Keywords are extracted from the query and contextual language embeddings are leveraged to predict semantically related words. Considering both their parts of speech and their word sense (most likely meaning given the keywords), the recommended words are finally mapped to corresponding pictograms. Based on preliminary evaluations (top-n category measure of agreement between word recommendations and keywords from partner’s message; case examples; visualizations of embeddings), the context-based prediction enhancements appear to have the potential to accelerate communication. This research is the first step in incorporating modern natural language models in AAC devices.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".