Papers with self-supervised contrastive learning method

2 papers
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction (2022.findings-acl)

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Challenge: Keyphrase extraction (KPE) extracts phrases in a document that provide a concise summary of the core content.
Approach: They propose an unsupervised keyphrase extraction method that ranks candidates by similarity between embeddings of source document and masked document.
Outcome: The proposed method outperforms state-of-the-art methods on six benchmarks . it achieves average 3.53 improvement over the existing method .
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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