Template-assisted Contrastive Learning of Task-oriented Dialogue Sentence Embeddings (2026.acl-long)
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| Challenge: | Annotating and gathering utterance relationships in dialogues is difficult, while token-level annotations, entities, slots and templates, are much easier to obtain. |
| Approach: | They propose a template-aware augmentation method that utilizes template information to learn utterance embeddings via self-supervised contrastive learning framework. |
| Outcome: | The proposed method improves on five benchmark dialogue datasets and shows that it is more efficient than previous SOTA methods. |
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