| 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 . |
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HierarchicalContrast: A Coarse-to-Fine Contrastive Learning Framework for Cross-Domain Zero-Shot Slot Filling (2023.findings-emnlp)
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| Challenge: | Existing zero-shot slot filling methods show limited generalization ability in target domain . et al., 2018: empirical results show that proposed method is better than existing methods . |
| Approach: | They propose a hierarchical contrastive learning framework for zero-shot slot filling . they use Gaussian-distributed embedding to learn generalized deep semantic relations . |
| Outcome: | The proposed method performs better than existing methods on unseen slot types . empirical results show that the proposed method can generalize to unseense slot types. |
Robust Zero-Shot Cross-Domain Slot Filling with Example Values (P19-1)
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| Challenge: | Task-oriented dialog systems rely on deep learning-based slot filling models . little to no training data for target domains may be available or schemas may not be aligned . |
| Approach: | They propose to use slot descriptions and examples of slot values to learn slot semantic representations that are transferable across domains and robust to misaligned schemas. |
| Outcome: | The proposed model outperforms state-of-the-art models on two multi-domain datasets on low-data setting. |
Slot Transferability for Cross-domain Slot Filling (2021.findings-acl)
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| Challenge: | Existing work on slot filling uses labeled data from source domains to train a model for target domains. |
| Approach: | They propose a model-agnostic Slot Transferability Measure (STM) to evaluate the transferability from a source slot to a target slot. |
| Outcome: | The proposed method outperforms state-of-the-art models on multiple datasets and models. |
Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling (2023.emnlp-main)
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| Challenge: | Recent research on slot filling has witnessed considerable improvement with considerable data and label shifts. |
| Approach: | They propose an adaptive end-to-end metric learning scheme for zero-shot slot filling that uses context-aware soft label representations and slot-level contrastive representation learning to mitigate the data and label shift problems. |
| Outcome: | The proposed approach outperforms existing methods on public benchmarks and shows that it is simple, efficient and generalizable. |
Synergistic Augmentation: Enhancing Cross-Domain Zero-Shot Slot Filling with Small Model-Assisted Large Language Models (2025.findings-acl)
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| Challenge: | Existing approaches to slot filling are limited due to data scarcity and timeconsuming efforts. |
| Approach: | They propose a framework that harnesses the power of a small model to augment inferential capabilities of LLMs without additional training. |
| Outcome: | The proposed framework improves slot filling performance on a spoken language dataset and a NER dataset. |
QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining (2021.acl-short)
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| Challenge: | Existing methods to predict slots and their values do not encode enough semantic information, limiting the models’ zero-shot capability. |
| Approach: | They propose a QA-driven slot filling model which extracts slot-filler spans from utterances with a span-based QA model. |
| Outcome: | The proposed model outperforms baselines by over 5% on the SNIPS benchmark. |
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. |
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling (2021.emnlp-main)
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| Challenge: | Existing methods for zero-shot cross-domain slot filling do not achieve effective knowledge transfer to the target domain. |
| Approach: | They propose a novel approach based on prototypical contrastive learning and a dynamic label confusion strategy for zero-shot slot filling. |
| Outcome: | The proposed model improves on unseen slots while setting new state-of-the-arts on slot filling task. |
AISFG: Abundant Information Slot Filling Generator (2022.naacl-main)
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| Challenge: | Existing approaches to zero/few-shot slot filling focus on slot descriptions and examples . AISFG model is based on domain-specific labels, which is not capable of transferring to new domains with little or no data. |
| Approach: | They propose a model with a query template that incorporates domain descriptions, slot descriptions, and examples with context. |
| Outcome: | Experimental results show that the proposed model outperforms state-of-the-art approaches in zero/few-shot slot filling 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. |