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# Who cites «Learning attention for historical text normalization by learning to pronounce»

16 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2017-bollmann-learning-attention-historical-text-normalization/)

1. [Cross-lingual Cross-temporal Summarization: Dataset, Models, Evaluation](https://aclanthology.org/2024.cl-3.5/) *— 2024 · Computational Linguistics*
2. [Historical German Text Normalization Using Type- and Token-Based Language Modeling](https://arxiv.org/abs/2409.02841) *— 2024 · arXiv:2409.02841*
3. [LLM-based Translation Across 500 Years. The Case for Early New High German](https://aclanthology.org/2024.konvens-main.37/) *— 2024 · Proceedings of the 20th Conference on Natural Language Processing (KONVENS 2024)*
4. [Event Extraction from Historical Texts: A New Dataset for Black Rebellions](https://aclanthology.org/2021.findings-acl.211/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
5. [Semi-supervised Contextual Historical Text Normalization](https://md.mova.elamur.ai/free/en/docs/papers/acl-2020-makarov-semi-supervised-contextual-historical-text/) *— our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
6. [A Large-Scale Comparison of Historical Text Normalization Systems](https://md.mova.elamur.ai/free/en/docs/papers/bollmann-2019-large-scale-comparison-historical-text/) *— our card · 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
7. [Few-Shot and Zero-Shot Learning for Historical Text Normalization](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2019-bollmann-few-shot-zero-shot-learning/) *— our card · 2019 · Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)*
8. [Historical Text Normalization with Delayed Rewards](https://md.mova.elamur.ai/free/en/docs/papers/acl-2019-flachs-historical-text-normalization-delayed-rewards/) *— our card · 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
9. [Towards Realistic Practices In Low-Resource Natural Language Processing: The Development Set](https://aclanthology.org/D19-1329/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
10. [An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization](https://aclanthology.org/C18-1112/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
11. [Evaluating Historical Text Normalization Systems: How Well Do They Generalize?](https://md.mova.elamur.ai/free/en/docs/papers/robertson-2018-evaluating-historical-text-normalization-systems/) *— our card · 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)*
12. [Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces](https://aclanthology.org/N18-1172/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*
13. [Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Setting](https://aclanthology.org/D18-1363/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
14. [Training Data Augmentation for Low-Resource Morphological Inflection](https://aclanthology.org/K17-2002/) *— 2017 · Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection*
15. granet-2018-decodeur-neuronal-pour-la-transcription
16. granet-2018-transfer-learning-letter-ngrams-word
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