Weakly Supervised Sentence Embeddings
Also known as: WS sentence embeddings, noisy-label sentence representation learning, weakly supervised sentence representation, distant-supervision sentence embeddings
Weakly supervised sentence embeddings train dense sentence representations using noisy, heuristic, or programmatically generated labels instead of costly human annotation. Labeling functions — rules, distant supervision signals, or lightweight classifiers — supply approximate supervision that a label model aggregates into probabilistic labels, which then guide the sentence encoder to produce task-useful representations at scale.
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When to use it
Use weakly supervised sentence embeddings when you have abundant unlabeled text, access to domain heuristics or structured knowledge bases, but limited budget for human annotation. Particularly valuable in specialized domains (biomedical, legal, financial) where off-the-shelf embeddings underperform and full annotation is prohibitively costly. Also appropriate when label noise is inevitable (social media, clinical notes) and probabilistic supervision better reflects uncertainty than hard labels. Do not use when high-quality labeled data is plentiful — fully supervised fine-tuning or standard sentence transformers will outperform; and do not use when the domain lacks reliable labeling functions, as low-quality or conflicting functions can degrade representation quality below even zero-shot baselines.
Strengths & limitations
- Scales to large corpora without proportional annotation cost by leveraging programmatic supervision.
- Integrates diverse weak signals — rules, KB lookups, model predictions — through principled label aggregation.
- Inherits rich linguistic priors from pre-trained transformer backbones, requiring less labeled data than training from scratch.
- Probabilistic labels explicitly model annotation uncertainty, reducing overconfident training on noisy signals.
- Embeddings generalize to multiple downstream tasks, spreading the annotation investment across applications.
- Compatible with active learning loops to iteratively refine labeling functions based on encoder feedback.
- Quality depends critically on the coverage and accuracy of labeling functions; poor functions yield degraded representations.
- Label model assumptions (conditional independence among functions) rarely hold perfectly, introducing systematic bias.
- Computational cost is higher than using static embeddings, requiring fine-tuning of large transformer backbones.
- Evaluation is difficult without clean gold labels; held-out test sets must be manually annotated to measure true performance.
- May underperform fully supervised fine-tuning when even moderate annotated data (a few hundred examples) is available.
Frequently asked
How many labeling functions do I need?
There is no fixed minimum, but label models work best with at least five to ten labeling functions that together cover a substantial fraction of the corpus. Prioritize diversity over quantity: functions that fire on different subsets of data improve the label model's ability to separate accuracy from correlation.
Can I use weakly supervised embeddings without a pre-trained transformer?
Yes, but pre-trained transformers (BERT, RoBERTa) provide strong initialization that compensates for noisy supervision. Training a sentence encoder from scratch under weak supervision requires substantially more data and more careful labeling function design to achieve comparable quality.
How do I know if my labeling functions are good enough?
Inspect coverage (fraction of sentences labeled), conflict rate (fraction where functions disagree), and empirical accuracy on a small manually annotated validation slice. High conflict with low coverage typically signals that functions need refinement before training.
Is this the same as self-supervised sentence embeddings?
No. Self-supervised methods (e.g., contrastive pre-training) generate supervision from the data structure itself — such as predicting masked tokens or using data augmentation — without any external label signal. Weakly supervised embeddings explicitly use external labeling functions encoding domain knowledge, typically for a specific downstream task.
What evaluation metric should I report?
Report performance on a manually annotated held-out test set using task-appropriate metrics: F1 (with macro or weighted averaging for multi-class), AUC-ROC for binary tasks, and — for retrieval — mean average precision or NDCG. Always report the size and source of the test annotation.
Sources
- Ratner, A., De Sa, C., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗
- Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP). DOI: 10.18653/v1/D19-1410 ↗
How to cite this page
ScholarGate. (2026, June 3). Weakly Supervised Sentence Embeddings. ScholarGate. https://scholargate.app/en/deep-learning/weakly-supervised-sentence-embeddings
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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- Self-supervised Sentence EmbeddingsDeep learning↔ compare
- Semi-supervised Sentence EmbeddingsDeep learning↔ compare
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- Transfer Learning with Sentence EmbeddingsDeep learning↔ compare
- Weakly supervised BERT-based classificationDeep learning↔ compare