Papers with dialogue representations

2 papers
DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling (2022.coling-1)

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Challenge: a conversational system can learn to rank response candidates for a given dialogue context by computing similarity between their vector representations.
Approach: They propose a framework that incorporates augmented dialogue contexts into the learning objective.
Outcome: The proposed framework outperforms existing methods and is more robust to perturbations seen during inference.
Learning Dialogue Representations from Consecutive Utterances (2022.naacl-main)

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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.
Approach: They propose a self-supervised contrastive learning method that learns dialogue representations suitable for a wide range of dialogue tasks.
Outcome: The proposed method outperforms baselines on five dialogue tasks on a few-shot and zero-shot datasets.

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