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Domain-adaptive sentence embeddings/Evidence
Method evidence record

Domain-adaptive sentence embeddings

Domain-adaptive sentence embeddings extend general-purpose sentence encoders — such as Sentence-BERT — by continuing their training on domain-specific text. The result is a fixed-length vector representation that captures both universal language understanding and the vocabulary, style, and semantic nuances of the target domain, improving downstream NLP tasks such as semantic search, clustering, and classification.

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Domain-Adaptive Sentence Embeddings (Domain-Adapted Sentence Transformers)
Taxonomic method record · ml-model / deep-learning
  • Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of EMNLP-IJCNLP 2019, pp. 3982–3992. · DOI 10.18653/v1/D19-1410
  • Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D. & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. Proceedings of ACL 2020, pp. 8342–8360. · DOI 10.18653/v1/2020.acl-main.740
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Related methods

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Taxonomic bucketBERT-based Classificationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketFine-Tuned Sentence Embeddingsmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMultilingual Sentence Embeddingsmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRoBERTa-based Classificationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSentence Embeddingsmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketTransfer Learning with Sentence Embeddingsmachine-suggested · Relational suggestion, not evidence.

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Sources

2 recorded citations, copied from the method source record.

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