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

Autor:	Jensen, Jonas Vestergaard, Jordahn, Mikkel, Andersen, Michael Riis
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning I.2.7
Druh dokumentu:	Working Paper
Popis:	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. Comment: 9 pages, 1 figure, 1 table, to be published in the proceedings of the Northern Lights Deep Learning Conference 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.09389 Zobrazit plný text záznamu View this record from Arxiv