Research map / Label-efficient and robust learning / Recognition and long-tail
An Integral Tag Recommendation Model for Textual Content
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add tag correlation (prediction path decoder) to encoder-only model (ablation) | ITAG1 · AU | F1@3: 0.071 -> 0.584 | Helped |
| add content-tag overlapping (shared embedding + indicator function) (ablation) | ITAG2 · AU | F1@3: 0.584 -> 0.657 | Helped |
| ITAG vs best baseline (TLSTM) F1@3 | ITAG · SO | F1@3: 0.480 -> 0.545 | Helped |
| ITAG vs best baseline (ABC) F1@3 | ITAG · Math | F1@3: 0.620 -> 0.746 | Helped |
| model content-tag overlapping, attention focus (ablation) | ITAG · SO visualization | attention focus | Helped |
| vary embedding size and GRU hidden size (ablation) | ITAG · Math | Recall@3/Precision@3 | No clear effect |
| concatenate vs add attention feature with decoder state (ablation) | ITAG · all | accuracy | No clear effect |
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