Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Explainable Sentence Embeddings× | BERT-gebaseerde Classificatie× | |
|---|---|---|
| Vakgebied | Deep learning | Deep learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2016–2018 | 2019 |
| Grondlegger≠ | Conneau et al.; Ribeiro et al. (probing + LIME frameworks) | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language) |
| Type≠ | Post-hoc interpretability applied to sentence encoders | Pre-trained language model with fine-tuning |
| Oorspronkelijke bron≠ | Conneau, A., Kruszewski, G., Lample, G., Barrault, L., & Baroni, M. (2018). What you can cram into a single $\vec{v}$ector: Probing sentence embeddings for linguistic properties. In Proceedings of ACL 2018, pp. 2126–2136. link ↗ | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics. DOI ↗ |
| Aliassen | interpretable sentence representations, XAI sentence embeddings, probing sentence embeddings, explainable sentence vectors | BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS |
| Verwant≠ | 6 | 4 |
| Samenvatting≠ | Explainable sentence embeddings combine dense sentence representation learning with post-hoc or intrinsic interpretability tools — such as probing classifiers, LIME, SHAP, or attention attribution — to reveal what linguistic and semantic information is encoded in a sentence vector and why a downstream model makes a given prediction. The goal is to retain the representational power of modern encoders while making their behavior auditable. | BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data. |
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