Learning to Rank with BERT for Argument Quality Evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The task of argument quality ranking, which identifies the quality of free text arguments, remains, to this day, a challenge. While most state-of-the-art initiatives use point-wise ranking methods and predict an absolute quality score for each argument, we instead focus on learning how to order them by their relative convincingness, experimenting with several learning-to-rank methods for argument quality. We leverage BERT's powerful ability in building a representation of an argument, paired with learning-to-rank approaches (point-wise, pairwise, list-wise) to rank arguments according to their measure of convincingness. We also demonstrate how an ensemble of models trained with different ranking losses often improves the performance at identifying the most convincing arguments of a list. Finally, we compare BERT coupled with learning-to-rank methods to state-of-the-art approaches on all major argument quality datasets available for the ranking task, demonstrating how a learning-to-rank approach generally performs better at outlining the topmost convincing arguments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| 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 it