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Neubig, Graham (8)
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The 2021 Conference on Empirical Methods in Natural Language Processing 2021 (4)
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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)
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2
Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
Pratapa, Adithya
;
Anastasopoulos, Antonios
;
Rijhwani, Shruti
. - : arXiv, 2021
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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
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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
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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
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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
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7
Dependency Induction Through the Lens of Visual Perception ...
Su, Ruisi
;
Rijhwani, Shruti
;
Zhu, Hao
. - : arXiv, 2021
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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
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