Proceedings of the First Workshop on Argumentation Mining
Bibliographic record
Abstract
Automated argumentation mining requires an adequate type system or annotation scheme for classifying the patterns of argument that succeed or fail in a corpus of legal documents.Moreover, there must be a reliable and accurate method for classifying the arguments found in natural language legal documents.Without an adequate and operational type system, we are unlikely to reach consensus on argument corpora that can function as a gold standard.This paper reports the preliminary results of research to annotate a sample of representative judicial decisions for the reasoning of the factfinder.The decisions report whether the evidence adduced by the petitioner adequately supports the claim that a medical theory causally links some type of vaccine with various types of injuries or adverse medical conditions.This paper summarizes and discusses some patterns of reasoning that we are finding, using examples from the corpus.The pattern types and examples presented here demonstrate the difficulty of developing a type or annotation system for characterizing the logically important patterns of reasoning.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.072 | 0.026 |
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".