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Actionable Email Intent Modeling With Reparametrized RNNs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use shared and domain-specific recurrent parameters with additive reparametrization | multidomain/multitask RNN · Average across E-T, E-A, I-T, and R-T | MACE: 0.325977602247 -> 0.320802130111 | Helped |
| use shared and domain-specific recurrent parameters with additive reparametrization | multidomain/multitask RNN · Email recipient action classification | F1: 31.4313323055 -> 32.7241147628 | Helped |
| add recurrent thread encoding (ablation) | Rainbow · Metadata-derived prediction tasks | MACE: 0.11627699018121548 -> 0.079575433335245443 | Helped |
| add attentive pooling instead of unweighted mean (ablation) | Rainbow · Metadata-derived prediction tasks | MACE: 0.080006082138796231 -> 0.079575433335245443 | Helped |
| use IRC data for transfer rather than email-only training | email recipient action classifier using Add representations · Email recipient action classification, E+I versus E | F1: 24.3328051165 -> 33.0636945739 | Helped |
| use Reddit data for transfer rather than email-only training | email recipient action classifier using Add representations · Email recipient action classification, E+R versus E | F1: 24.3328051165 -> 25.9690487907 | Helped |
Closest papers by meaning
- MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations (ICLR 2024 · similarity 0.80)
- Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization (NeurIPS 2026 · similarity 0.80)
- EmFORE: Learning Email Folder Classification Rules by Demonstration (AAAI 2024 · similarity 0.80)
- ACT as Human: Multimodal Large Language Model Data Annotation with Critical Thinking (NeurIPS 2025 · similarity 0.80)
- ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation (ICML 2025 · similarity 0.80)
- Uni-MIS: United Multiple Intent Spoken Language Understanding via Multi-View Intent-Slot Interaction (AAAI 2024 · similarity 0.79)
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