Papers with contrastive learning strategies
Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog (2023.findings-acl)
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| Challenge: | Empirical results show dialog context representations are more conducive to multi-turn task-oriented dialog. |
| Approach: | They propose an end-to-end task-oriented dialog system with two contrastive learning strategies to model relationship between dialog context and belief/action state representations. |
| Outcome: | Empirical results show that dialog context representations are more conducive to multi-turn task-oriented dialog. |
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs (2023.emnlp-main)
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| Challenge: | Recent studies on relation representation learning focus on contrastive learning strategies, but these studies overlook important aspects. |
| Approach: | They propose to use within-sentence pairs augmentation and cross-sentent pairs extraction to increase diversity of positive pairs and strengthen the discriminative power of contrastive learning. |
| Outcome: | The proposed task increases diversity of positive pairs and strengthens discriminative power . it overcomes limitations of traditional Relation Extraction tasks, which require manual annotations . |
LA-UCL: LLM-Augmented Unsupervised Contrastive Learning Framework for Few-Shot Text Classification (2024.lrec-main)
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| Challenge: | Experimental results show that our model exceeds the baseline models due to the lack of cognitive ability. |
| Approach: | They propose a LLM-Augmented Unsupervised Contrastive Learning Framework which introduces a cognition-enabled Large Language Model (LLM) for efficient data augmentation and presents corresponding contrastive learning strategies. |
| Outcome: | The proposed model exceeds baseline models on six datasets. |
LCAN: A Label-Aware Contrastive Attention Network for Multi-Intent Recognition and Slot Filling in Task-Oriented Dialogue Systems (2025.findings-emnlp)
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| Challenge: | Multi-intent utterances processing remains a persistent challenge due to intricate intent-slot dependencies and semantic ambiguities. |
| Approach: | They propose a label-aware contrastive attention network (LCAN) that integrates label-based attention and contrastive learning strategies to improve semantic understanding and generalization in multi-intent scenarios. |
| Outcome: | The proposed model improves intent recognition and slot filling performance in multi-intent dialogue systems. |