Previous work in phonetically-grounded language generation has mainly focused
on domains such as lyrics and poetry. In this paper, we present work on the
generation of tongue twisters - a form of language that is required to be
phonetically conditioned to maximise sound overlap, whilst maintaining semantic
consistency with an input topic, and still being grammatically correct. We
present \textbf{TwistList}, a large annotated dataset of tongue twisters,
consisting of 2.1K+ human-authored examples. We additionally present several
benchmark systems (referred to as TwisterMisters) for the proposed task of
tongue twister generation, including models that both do and do not require
training on in-domain data. We present the results of automatic and human
evaluation to demonstrate the performance of existing mainstream pre-trained
models in this task with limited (or no) task specific training and data, and
no explicit phonetic knowledge. We find that the task of tongue twister
generation is challenging for models under these conditions, yet some models
are still capable of generating acceptable examples of this language type.

本文主要研究了语音基础的语言生成在诸如绕口令等领域的应用，提出了 TwistList 数据集以及多个适用于绕口令生成的 benchmark 系统，经过自动和人工评估，发现模型在无特定训练和数据、无显式语音知识的情况下，生成绕口令的表现仍需提升。

基于音系学的语言生成：绕口令案例研究

Phonetically-Grounded Language Generation: The Case of Tongue Twisters

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。