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Predicting Readers' Sarcasm Understandability by Modeling Gaze Behavior
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add textual features to gaze features (ablation) | MILR classifier · sarcasm understandability, all domains | weighted avg F-score: 87.5 -> 93 | Helped |
| add textual features to gaze features (ablation) | MILR classifier · sarcasm understandability, all domains | F-score, sarcasm miss class: 50 -> 72 | Helped |
| use gaze features instead of reading time as predictor (ablation) | MILR classifier · sarcasm understandability, all domains | weighted avg F-score: 80 -> 93 | Helped |
| MILR with gaze features instead of class-frequency baseline | sarcasm understandability prediction · all domains | Kappa: 0.014 -> 0.6845 | Helped |
| use multi-instance learning instead of standard SVM/logistic regression (ablation) | sarcasm understandability prediction · all domains | F-score: as low as 30% -> 93 | Helped |
| increase training data from 70% to 100% (ablation) | MILR classifier · sarcasm understandability | F-score | Helped |
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