Grammaticality Judgement in a Word Completion Task
Bibliographic record
Abstract
In this paper, we present findings from a human judgement task we conducted on the effectiveness of syntax filtering in a word completion task. Human participants were asked to review a series of incomplete sentences and identify which words from accompanying lists extend the expressions in a grammatically appropriate way. The accompanying word lists were generated by two word completion systems (our own plus a third-party commercial system) where the ungrammatical items were filtered out. Overall, participants agreed more, to a statistically significant degree, with the syntax-filtered systems than with baseline. However, further analysis suggests that syntax filtering alone does not necessarily improve the overall acceptability and usability of the word completion output. Given that word completion is typically employed in applications to aid writing, unlike other NLP tasks, accounting for the role of writer vs. reader becomes critical. Evaluating word completion and, more generally, applications for alternative and augmentative communication (AAC) will be discussed.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".