Triple-Encoders: Representations That Fire Together, Wire Together (2024.acl-long)
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| Challenge: | Existing search-based dialog models re-encode the dialog history at every turn, causing high cost. |
| Approach: | They propose a method that encodes relative distances between utterances via a bi-encoder . they use a hebbian-inspired co-occurrence learning objective to compute distributed utterrance mixtures . |
| Outcome: | The proposed method improves on bi-encoded utterances and even better generalization . it can encode 2020 and Erker et al. (2023) with the same results. |
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| Challenge: | Curved Contrastive Learning is a new approach for learning the relative turn distance between utterance pairs in multi-turn dialogues. |
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| Challenge: | Dialogue Sentence Embedding (DSE) is a self-supervised contrastive learning method that learns effective dialogue representations suitable for a wide range of dialogue-oriented tasks. |
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