Bert-based Braille Summarization of Long Documents

Yamuna, K.; Shriamrut, V.; Singh, Drishti; Gopalasamy, Vaishnavi; Menon, Vivek

doi:10.1109/icccnt51525.2021.9579748

Cited by 8 publications

(1 citation statement)

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“…An average improvement of 13% for each of these evaluation metrics was observed. BERT, additionally, has been employed for many other text processing tasks such as summarization of braille documents [41], dependency parsing for Tamil langauge [42] & aspect term extraction for sentiment analysis [43. Chinese Spelling Check (CSC) is a challenging task due to the complex characteristics of Chinese characters. The most common errors present in Chinese language are phonological or visual errors.…”

Section: Literature Surveymentioning

confidence: 99%

Grammatical versus Spelling Error Correction: An Investigation into the Responsiveness of Transformer-Based Language Models Using BART and MarianMT

Raju,

Pati,

Gandheesh

et al. 2024

J. Info. Know. Mgmt.

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Text continues to remain a relevant form of representation for information. Text documents are created either in digital native platforms or through the conversion of other media files such as images and speech. While the digital native text is invariably obtained through physical or virtual keyboards, technologies such as OCR and speech recognition are utilised to transform the images and speech signals into text content. All these variety of mechanisms of text generation also introduce errors into the captured text. This project aims at analysing different kinds of errors that occur in text documents. The work employs two of the advanced deep neural network-based language models, namely, BART and MarianMT, to rectify the anomalies present in the text. Transfer learning of these models with available dataset is performed to finetune their capacity for error correction. A comparative study is conducted to investigate the effectiveness of these models in handling each of the defined error categories. It is observed that while both models can bring down the erroneous sentences by 20+%, BART can handle spelling errors far better (24.6%) than grammatical errors (8.8%).

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Section: Literature Surveymentioning

confidence: 99%