Papers with cross-domain slot filling

3 papers
Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling (2020.acl-main)

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Challenge: Existing approaches to slot filling are expensive and time-consuming.
Approach: They propose a Coarse-to-fine approach for cross-domain slot filling . they propose utterance templates to regularize the representation of utterrances .
Outcome: The proposed model outperforms state-of-the-art approaches in slot filling . it can be applied to the cross-domain named entity recognition task .
Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting (2023.findings-acl)

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Challenge: Existing approaches to slot filling only learn surface mapping of slot types between D S and D T and get poor generalization capability or robustness.
Approach: They propose a generative zero-shot prompt learning framework for cross-domain slot filling which improves generalization and robustness than previous work.
Outcome: The proposed framework improves generalization and robustness on unseen slots and an efficient prompt tuning strategy boosts performance.
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack (2020.coling-main)

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Challenge: Existing approaches to zero-shot slot filling ignore constraints in the latent space and lack robustness.
Approach: They propose a Contrastive Zero-Shot Learning with Adversarial Attack method for slot filling . they propose to map slot value contextual representations to slot description representations .
Outcome: The proposed method outperforms state-of-the-art models under zero-shot and few-shot settings.

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