Research map / Label-efficient and robust learning / Domain adaptation and generalization
Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| HATNh vs best baseline AMN | cross-domain sentiment · Amazon reviews | accuracy: 0.8279 -> 0.8661 | Helped |
| add NP-net (non-pivot network) to P-net (ablation) | P-net · Amazon reviews | accuracy: 0.8556 -> 0.8620 | Helped |
| add P-net to NP-net (ablation) | NP-net · Amazon reviews | accuracy: 0.7949 -> 0.8620 | Helped |
| add hierarchical positional encoding (ablation) | HATN · Amazon reviews | accuracy: 0.8620 -> 0.8661 | Helped |
| add word-level attention to sentence-only attention (hierarchical) (ablation) | P-net · Amazon reviews | accuracy: 83.66% -> 0.8556 | Helped |
| use hierarchical attention instead of word-only attention (AMN) | P-net · Amazon reviews | accuracy: 0.8279 -> 0.8556 | Helped |
| add hierarchical positional encoding (ablation) | HATN · Amazon reviews E->V | accuracy: 0.8455 -> 0.8418 | Hurt |
Closest papers by meaning
- Interactive Attention Transfer Network for Cross-Domain Sentiment Classification (AAAI 2019 · similarity 0.93)
- Simultaneous Learning of Pivots and Representations for Cross-Domain Sentiment Classification (AAAI 2020 · similarity 0.88)
- Exploiting Coarse-to-Fine Task Transfer for Aspect-Level Sentiment Classification (AAAI 2019 · similarity 0.84)
- An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis (AAAI 2021 · similarity 0.84)
- Structural Correspondence Learning for Cross-Lingual Sentiment Classification with One-to-Many Mappings (AAAI 2017 · similarity 0.83)
- Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms (AAAI 2017 · similarity 0.83)
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