Short descriptive answer evaluation using word-embedding techniques

Sreevidhya, V; Narayanan, Jayasree

doi:10.1109/icccnt51525.2021.9579636

Cited by 7 publications

(1 citation statement)

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“…Sreevidhya & Narayanan [28] have employed three vectorization techniques, namely, Latent Semantic Indexing (LSI), Sentence-BERT & Word2Vec to evaluate the answers by students. These answers are compared against the model answer using these embedding techniques.…”

Section: Tan Et Al'smentioning

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: Tan Et Al'smentioning

confidence: 99%