Tongue twisters are meaningful sentences that are difficult to pronounce. The
process of automatically generating tongue twisters is challenging since the
generated utterance must satisfy two conditions at once: phonetic difficulty
and semantic meaning. Furthermore, phonetic difficulty is itself hard to
characterize and is expressed in natural tongue twisters through a
heterogeneous mix of phenomena such as alliteration and homophony. In this
paper, we propose PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue
Twisters Automatically. We leverage phoneme representations to capture the
notion of phonetic difficulty, and we train language models to generate
original tongue twisters on two proposed task settings. To do this, we curate a
dataset called PANCETTA, consisting of existing English tongue twisters.
Through automatic and human evaluation, as well as qualitative analysis, we
show that PANCETTA generates novel, phonetically difficult, fluent, and
semantically meaningful tongue twisters.

本文介绍了一个名为 PANCETTA 的自然语言处理模型，能自动生成切题难度适中但有意义的绕口令并提供了基于 PANCETTA 模型自创的数据集，经过自动和人工评估及定性分析证明，PANCETTA 模型可以生成新颖的，语音难度适中的，流利的和意义深远的绕口令。