Happy Together: Learning and Understanding Appraisal From Natural\n Language
Bibliographic record
Abstract
In this paper, we explore various approaches for learning two types of\nappraisal components from happy language. We focus on 'agency' of the author\nand the 'sociality' involved in happy moments based on the HappyDB dataset. We\ndevelop models based on deep neural networks for the task, including uni- and\nbi-directional long short-term memory networks, with and without attention. We\nalso experiment with a number of novel embedding methods, such as embedding\nfrom neural machine translation (as in CoVe) and embedding from language models\n(as in ELMo). We compare our results to those acquired by several traditional\nmachine learning methods. Our best models achieve 87.97% accuracy on agency and\n93.13% accuracy on sociality, both of which are significantly higher than our\nbaselines.\n
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".