Neural Transduction of Letter Position Dyslexia using an Anagram Matrix Representation

Avi Bleiweiss


Abstract
Research on analyzing reading patterns of dyslectic children has mainly been driven by classifying dyslexia types offline. We contend that a framework to remedy reading errors inline is more far-reaching and will help to further advance our understanding of this impairment. In this paper, we propose a simple and intuitive neural model to reinstate migrating words that transpire in letter position dyslexia, a visual analysis deficit to the encoding of character order within a word. Introduced by the anagram matrix representation of an input verse, the novelty of our work lies in the expansion from one to a two dimensional context window for training. This warrants words that only differ in the disposition of letters to remain interpreted semantically similar in the embedding space. Subject to the apparent constraints of the self-attention transformer architecture, our model achieved a unigram BLEU score of 40.6 on our reconstructed dataset of the Shakespeare sonnets.
Anthology ID:
2020.bionlp-1.16
Volume:
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing
Month:
July
Year:
2020
Address:
Online
Editors:
Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
Venue:
BioNLP
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
150–155
Language:
URL:
https://aclanthology.org/2020.bionlp-1.16
DOI:
10.18653/v1/2020.bionlp-1.16
Bibkey:
Cite (ACL):
Avi Bleiweiss. 2020. Neural Transduction of Letter Position Dyslexia using an Anagram Matrix Representation. In Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, pages 150–155, Online. Association for Computational Linguistics.
Cite (Informal):
Neural Transduction of Letter Position Dyslexia using an Anagram Matrix Representation (Bleiweiss, BioNLP 2020)
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PDF:
https://aclanthology.org/2020.bionlp-1.16.pdf