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Deep active learning (DAL) seeks to reduce annotation costs by enabling the model to actively query instance annotations from which it expects to learn the most.
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Aßenmacher, M., Heumann, C.: On the comparability of pre-trained language models. In: Proceedings of the 5th Swiss Text Analytics Conference and 16th Conference on Natural Language Processing. CEUR Workshop Proceedings, Zurich, Switzerland (Online) (Jun 2020), http://ceur-ws.org/Vol-2624/paper2.pdf
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Schick, T., Schütze, H.: It’s not just size that matters: Small language models are also few-shot learners. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 2339–2352. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.naacl-main.185, https://aclanthology.org/2021.naacl-main.185
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Tan, W., Du, L., Buntine, W.: Diversity Enhanced Active Learning with Strictly Proper Scoring Rules. In: Advances in Neural Information Processing Systems (NeurIPS)). vol. 35 (2021)
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Casanueva, I., Temčinas, T., Gerz, D., Henderson, M., Vulić, I.: Efficient intent detection with dual sentence encoders. In: Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI. pp. 38–45. Association for Computational Linguistics, Online (Jul 2020). https://doi.org/10.18653/v1/2020.nlp4convai-1.5, https://aclanthology.org/2020.nlp4convai-1.5
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Ru, D., Feng, J., Qiu, L., Zhou, H., Wang, M., Zhang, W., Yu, Y., Li, L.: Active sentence learning by adversarial uncertainty sampling in discrete space. In: Findings of the Association for Computational Linguistics: EMNLP 2020. pp. 4908–4917. Association for Computational Linguistics, Online (Nov 2020). https://doi.org/10.18653/v1/2020.findings-emnlp.441, https://aclanthology.org/2020.findings-emnlp.441
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