Papers by Keith Vertanen

2 papers
Accelerating Text Communication via Abbreviated Sentence Input (2021.acl-long)

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Challenge: Skipping spaces or other characters may be able to speed input and reduce a user’s physical input effort.
Approach: They designed a neural language model to expand noisy abbreviated input where users often omit spaces and mid-word vowels.
Outcome: The proposed recognizer can expand noisy abbreviated input even if a third of characters is omitted.
Adapting Large Language Models for Character-based Augmentative and Alternative Communication (2025.findings-emnlp)

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Challenge: Most character language models predict subword tokens of variable length .
Approach: They propose to use large pretrained character language models to make accurate character predictions.
Outcome: The proposed method produces more accurate character predictions than classification models and n-gram models.

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