DeCLUTR

2000년 도입 · 논문 3편에서 사용

DeCLUTR is an approach for learning universal sentence embeddings that utilizes a self-supervised objective that does not require labelled training data. The objective learns universal sentence embeddings by training an encoder to minimize the distance between the embeddings of textual segments randomly sampled from nearby in the same document.

출처: DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations

소개 논문: DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations