Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases

Understand

Previous work in phonologically and phonetically grounded language generation has mainly focused on domains such as puns and poetry.

• In this article, we present new work on the generation of English tongue twisters - a form of language that is required to be conditioned on a phoneme level to maximize sound overlap, while maintaining semantic consistency with an input topic or phrase and still being grammatically correct.
• We present TwisterLister, a pipeline for generating phonologically informed tongue twisters from large language models (LLMs) that we use to generate TwistList 2.0, the largest annotated dataset of tongue twisters to date, consisting of 17K+ examples from a combination of human and LLM authors.
• Our generation pipeline involves the use of a phonologically constrained vocabulary alongside LLM prompting to generate novel, non-derivative tongue twister examples.