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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 MasakhaNER: Named entity recognition for African languages Adelani, David,; Abbott, Jade; Neubig, Graham... In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03350962 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, ⟨10.1162/tacl⟩ (2021) BASE Show details 2 Evaluating the Morphosyntactic Well-formedness of Generated Texts ... Pratapa, Adithya; Anastasopoulos, Antonios; Rijhwani, Shruti. - : arXiv, 2021 BASE Show details 3 Explorations in Transfer Learning for OCR Post-Correction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Neubig, Graham; Rijhwani, Shruti. - : Underline Science Inc., 2021 BASE Show details 4 Evaluating the Morphosyntactic Well-formedness of Generated Texts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Anastasopoulos, Antonios; Chaudhary, Aditi. - : Underline Science Inc., 2021 BASE Show details 5 Lexically Aware Semi-Supervised Learning for OCR Post-Correction ... Rijhwani, Shruti; Rosenblum, Daisy; Anastasopoulos, Antonios; Neubig, Graham. - : arXiv, 2021 Abstract: Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents. Optical character recognition (OCR) can be used to produce digitized text, and previous work has demonstrated the utility of neural post-correction methods that improve the results of general-purpose OCR systems on recognition of less-well-resourced languages. However, these methods rely on manually curated post-correction data, which are relatively scarce compared to the non-annotated raw images that need to be digitized. In this paper, we present a semi-supervised learning method that makes it possible to utilize these raw images to improve performance, specifically through the use of self-training, a technique where a model is iteratively trained on its own outputs. In addition, to enforce consistency in the recognized vocabulary, we introduce a lexically-aware decoding method that augments the neural post-correction model with a count-based language model constructed from the ... : Accepted to the Transactions of the Association for Computational Linguistics (TACL) ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2111.02622 https://dx.doi.org/10.48550/arxiv.2111.02622 BASE Hide details 6 Lexically-Aware Semi-Supervised Learning for OCR Post-Correction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Anastasopoulos, Antonios; Neubig, Graham. - : Underline Science Inc., 2021 BASE Show details 7 Dependency Induction Through the Lens of Visual Perception ... Su, Ruisi; Rijhwani, Shruti; Zhu, Hao. - : arXiv, 2021 BASE Show details 8 Dependency Induction Through the Lens of Visual Perception ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Hao; Bisk, Yonatan. - : Underline Science Inc., 2021 BASE Show details

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