LADy 💃: A Benchmark Toolkit for Latent Aspect Detection Enriched with Backtranslation Augmentation
Bibliographic record
Abstract
We present LADy , a Python-based benchmark toolkit to facilitate extracting aspects of products or services in reviews toward which customers target their opinions and sentiments.While there has been a significant increase in aspect-based sentiment analysis, yet the proposed methods' practical implications in real-world settings remain moot for their closed and irreproducible codebases, inability to accommodate datasets from various domains, and poor evaluation methodologies.LADy is an open-source benchmark toolkit with a standard pipeline and experimental details to fill the gaps.It incorporates a host of canonical models along with benchmark datasets from varying domains, including unsolicited online reviews.Leveraging an object-oriented design, LADy readily extends to new models and training datasets.The first of its kind, LADy also features review augmentation via natural language backtranslation that can be integrated into the training phase of the models to boost efficiency and improve efficacy during inference.LADy's codebase, along with the installation instructions and case studies on five datasets for seven methods with backtranslation augmentation over ten languages, can be obtained under cc-by-nc-sa-4.0license at https://github.com/fani-lab/LADy.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.017 |
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".