Kavli Affiliate: Michael P. Brenner
| First 5 Authors: Shanqing Cai, Subhashini Venugopalan, Katrin Tomanek, Ajit Narayanan, Meredith R. Morris
| Summary:
Motivated by the need for accelerating text entry in augmentative and
alternative communication (AAC) for people with severe motor impairments, we
propose a paradigm in which phrases are abbreviated aggressively as primarily
word-initial letters. Our approach is to expand the abbreviations into
full-phrase options by leveraging conversation context with the power of
pretrained large language models (LLMs). Through zero-shot, few-shot, and
fine-tuning experiments on four public conversation datasets, we show that for
replies to the initial turn of a dialog, an LLM with 64B parameters is able to
exactly expand over 70 of phrases with abbreviation length up to 10, leading to
an effective keystroke saving rate of up to about 77 on these exact expansions.
Including a small amount of context in the form of a single conversation turn
more than doubles abbreviation expansion accuracies compared to having no
context, an effect that is more pronounced for longer phrases. Additionally,
the robustness of models against typo noise can be enhanced through fine-tuning
on noisy data.
| Search Query: ArXiv Query: search_query=au:”Michael P. Brenner”&id_list=&start=0&max_results=10