Research map / Large language models / Retrieval and knowledge
Zero-Resource Knowledge-Grounded Dialogue Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use zero-resource variational learning with dialogue and knowledge corpora that are independent | ZRKGC · Wizard Seen and Wizard Unseen | F1: 18.7 vs 19.3; 18.6 vs 17.9 | Mixed |
| Use zero-resource variational learning with independent dialogue and knowledge corpora | ZRKGC · Topical Freq, Topical Rare, and CMU_DoG | F1: 16.6 vs 16.5; 16.8 vs 15.1; 12.5 vs 10.7 | Helped |
| Add the grounding-rate latent variable Zα (ablation) | ZRKGC · Wizard Seen | F1: 18.5 -> 18.7 | Helped |
| Add the mutual information loss (ablation) | ZRKGC · Wizard Seen | F1: 18.1 -> 18.7 | Helped |
| Use a retrieval posterior instead of a generative posterior (ablation) | ZRKGC · Wizard Seen | F1: 16.2 -> 18.7 | Helped |
| Parameterize the posterior over retrieved knowledge candidates rather than use only the top candidate (ablation) | ZRKGC · Wizard Seen | F1: 17.2 -> 18.7 | Helped |
| Use the learned knowledge selection module rather than remove it (ablation) | ZRKGC · Wizard Seen | F1: 18.3 -> 18.7 | Helped |
Closest papers by meaning
- Low-Resource Knowledge-Grounded Dialogue Generation (ICLR 2020 · similarity 0.94)
- KPT: Keyword-Guided Pre-training for Grounded Dialog Generation (AAAI 2023 · similarity 0.92)
- A Knowledge-Grounded Neural Conversation Model (AAAI 2018 · similarity 0.91)
- Improving Open-Domain Dialogue Response Generation with Multi-Source Multilingual Commonsense Knowledge (AAAI 2024 · similarity 0.90)
- Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive Learning (AAAI 2024 · similarity 0.89)
- From Retrieval to Generation: A Simple and Unified Generative Model for End-to-End Task-Oriented Dialogue (AAAI 2024 · similarity 0.88)
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