مقایسهٔ روش‌ها

	آموزش تعبیه‌سازی جملات با نظارت ضعیف	طبقه‌بندی مبتنی بر BERT با نظارت ضعیف
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	2016–2019	2017–2020
پدیدآور	Ratner et al. (weak supervision framework); Reimers & Gurevych (sentence embeddings)	Multiple (Ratner et al. for weak supervision framework; Meng et al. for BERT integration)
نوع	Representation learning under weak supervision	Weakly supervised fine-tuning of pre-trained language model
منبع بنیادین	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.	Meng, Y., Zhang, Y., Huang, J., Xiong, C., Ji, H., Zhang, C., & Han, J. (2020). Text Classification Using Label Names Only: A Language Model Self-Training Approach. Proceedings of EMNLP 2020, 9006–9017.
نام‌های دیگر	WS sentence embeddings, noisy-label sentence representation learning, weakly supervised sentence representation, distant-supervision sentence embeddings	WS-BERT, BERT with weak supervision, label-efficient BERT classification, noisy-label BERT fine-tuning
مرتبط	6	6
خلاصه	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.	Weakly supervised BERT-based classification adapts BERT to text classification tasks when only noisy, heuristic, or programmatically generated labels are available instead of clean human annotations. It combines weak supervision frameworks — such as labeling functions and data programming — with BERT's pre-trained language representations to achieve robust classification without expensive hand-labeling.