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arXiv · 2311.09389

Neural machine translation for automated feedback on children's early-stage writing

Jonas Vestergaard Jensen·Mikkel Jordahn·Michael Riis Andersen
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Abstract

In this work, we address the problem of assessing and constructing feedback for early-stage writing automatically using machine learning. Early-stage writing is typically vastly different from conventional writing due to phonetic spelling and lack of proper grammar, punctuation, spacing etc. Consequently, early-stage writing is highly non-trivial to analyze using common linguistic metrics. We propose to use sequence-to-sequence models for "translating" early-stage writing by students into "conventional" writing, which allows the translated text to be analyzed using linguistic metrics. Furthermore, we propose a novel robust likelihood to mitigate the effect of noise in the dataset. We investigate the proposed methods using a set of numerical experiments and demonstrate that the conventional text can be predicted with high accuracy.

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Jonas Vestergaard Jensen, Mikkel Jordahn, Michael Riis Andersen. 2023-11-15. Neural machine translation for automated feedback on children's early-stage writing. https://arxiv.org/abs/2311.09389

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