DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations
Read paper: https://www.aclanthology.org/2021.acl-long.72 Abstract: Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various downstream tasks...
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ftdatacite:10.48448/kfq5-k490 2023-05-15T16:01:59+02:00 DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021 Bader, Gary Giorgi, John Nitski, Osvald Wang, Bo 2021 https://dx.doi.org/10.48448/kfq5-k490 https://underline.io/lecture/25949-declutr-deep-contrastive-learning-for-unsupervised-textual-representations unknown Underline Science Inc. Neural Network Electromagnetism Computational Linguistics Condensed Matter Physics FOS Physical sciences Deep Learning Semantics Information and Knowledge Engineering Audiovisual article MediaObject Conference talk 2021 ftdatacite https://doi.org/10.48448/kfq5-k490 2022-02-08T17:44:38Z Read paper: https://www.aclanthology.org/2021.acl-long.72 Abstract: Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various downstream tasks, such as clustering and retrieval. Unlike word embeddings, the highest performing solutions for learning sentence embeddings require labelled data, limiting their usefulness to languages and domains where labelled data is abundant. In this paper, we present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations. Inspired by recent advances in deep metric learning (DML), we carefully design a self-supervised objective for learning universal sentence embeddings that does not require labelled training data. When used to extend the pretraining of transformer-based language models, our approach closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders. Importantly, our experiments suggest that the quality of the learned embeddings scale with both the number of trainable parameters and the amount of unlabelled training data. Our code and pretrained models are publicly available and can be easily adapted to new domains or used to embed unseen text. Article in Journal/Newspaper DML DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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Neural Network Electromagnetism Computational Linguistics Condensed Matter Physics FOS Physical sciences Deep Learning Semantics Information and Knowledge Engineering |
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Neural Network Electromagnetism Computational Linguistics Condensed Matter Physics FOS Physical sciences Deep Learning Semantics Information and Knowledge Engineering The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021 Bader, Gary Giorgi, John Nitski, Osvald Wang, Bo DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations |
topic_facet |
Neural Network Electromagnetism Computational Linguistics Condensed Matter Physics FOS Physical sciences Deep Learning Semantics Information and Knowledge Engineering |
description |
Read paper: https://www.aclanthology.org/2021.acl-long.72 Abstract: Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various downstream tasks, such as clustering and retrieval. Unlike word embeddings, the highest performing solutions for learning sentence embeddings require labelled data, limiting their usefulness to languages and domains where labelled data is abundant. In this paper, we present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations. Inspired by recent advances in deep metric learning (DML), we carefully design a self-supervised objective for learning universal sentence embeddings that does not require labelled training data. When used to extend the pretraining of transformer-based language models, our approach closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders. Importantly, our experiments suggest that the quality of the learned embeddings scale with both the number of trainable parameters and the amount of unlabelled training data. Our code and pretrained models are publicly available and can be easily adapted to new domains or used to embed unseen text. |
format |
Article in Journal/Newspaper |
author |
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021 Bader, Gary Giorgi, John Nitski, Osvald Wang, Bo |
author_facet |
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021 Bader, Gary Giorgi, John Nitski, Osvald Wang, Bo |
author_sort |
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021 |
title |
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations |
title_short |
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations |
title_full |
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations |
title_fullStr |
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations |
title_full_unstemmed |
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations |
title_sort |
declutr: deep contrastive learning for unsupervised textual representations |
publisher |
Underline Science Inc. |
publishDate |
2021 |
url |
https://dx.doi.org/10.48448/kfq5-k490 https://underline.io/lecture/25949-declutr-deep-contrastive-learning-for-unsupervised-textual-representations |
genre |
DML |
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DML |
op_doi |
https://doi.org/10.48448/kfq5-k490 |
_version_ |
1766397634674688000 |