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Hits 1 – 4 of 4
1
SD-QA: Spoken Dialectal Question Answering for the Real World ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Alam, Md Mahfuz Ibn
;
Anastasopoulos, Antonios
. - : Underline Science Inc., 2021
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2
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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3
When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Anastasopoulos, Antonios
;
Chaudhary, Aditi
. - : Underline Science Inc., 2021
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4
Lexically-Aware Semi-Supervised Learning for OCR Post-Correction ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Anastasopoulos, Antonios
;
Neubig, Graham
;
Rijhwani, Shruti
;
Rosenblum, Daisy
. - : Underline Science Inc., 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 ...
Keyword:
Computational Linguistics
;
Machine Learning
;
Machine Learning and Data Mining
;
Natural Language Processing
URL:
https://dx.doi.org/10.48448/fycy-h885
https://underline.io/lecture/38192-lexically-aware-semi-supervised-learning-for-ocr-post-correction
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