Cause-Effect Knowledge Acquisition and Neural Association Model for Solving A Set of Winograd Schema Problems
Why this work is in the frame
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Bibliographic record
Abstract
This paper focuses on the investigations in Winograd Schema (WS), a challenging problem which has been proposed for measuring progress in commonsense reasoning.Due to the lack of commonsense knowledge and training data, very little work has been found on the WS problems in recent years.Actually, there is no shortcut to solve this problem except to collect more commonsense knowledge and design suitable models.Therefore, this paper addresses a set of WS problems by proposing a knowledge acquisition method and a general neural association model.To avoid the sparseness issue, the knowledge we aim to collect is the cause-effect relationships between thousands of commonly used words.The knowledge acquisition method supports us to extract hundreds of thousands of cause-effect pairs from large text corpus automatically.Meanwhile, a neural association model (NAM) is proposed to encode the association relationships between any two discrete events.Based on the extracted knowledge and the NAM models, in this paper, we successfully build a system for solving WS problems from scratch and achieve 70.0% accuracy.Most importantly, this paper provides a flexible framework to solve WS problems based on event association and neural network methods.
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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) | 0.000 | 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 | 0.001 |
| 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 it