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Explorations in Transfer Learning for OCR Post-Correction ...
Abstract: We explore transfer learning to improve optical character recognition (OCR) post-correction, specifically for endangered language texts. We extend an existing OCR post-correction model (Rijhwani et al., 2020) by introducing an additional pretraining step on related data, such as text in a related language or available target endangered language datasets that may differ in orthography. Although cross-lingual transfer is often successful in high-resource settings, our preliminary results show that transferring from related language data decreases performance for this task. On the other hand, we observe small improvements in performance when transferring from additional target language data. ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
URL: https://dx.doi.org/10.48448/qwdn-bd11
https://underline.io/lecture/39677-explorations-in-transfer-learning-for-ocr-post-correction
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2
Efficient Test Time Adapter Ensembling for Low-resource Language Varieties ...
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3
Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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Distributionally Robust Multilingual Machine Translation ...
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5
When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection ...
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6
Lexically-Aware Semi-Supervised Learning for OCR Post-Correction ...
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7
Phrase-level Active Learning for Neural Machine Translation ...
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8
Dependency Induction Through the Lens of Visual Perception ...
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