# PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically

arXiv: https://arxiv.org/abs/2209.06275 · HTML: https://mlsanity.com/paper/2209.06275/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 59 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 21 of 21)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [8-bit Optimizers via Block-wise Quantization](https://mlsanity.com/paper/2110.02861/) (2021)
- [Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation](https://mlsanity.com/paper/1911.03842/) (2019)
- [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://mlsanity.com/paper/2109.11680/) (2021)
- [MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding](https://mlsanity.com/paper/2103.06779/) (2021)
- [GenAug: Data Augmentation for Finetuning Text Generators](https://mlsanity.com/paper/2010.01794/) (2020)
- [Syntactic Scaffolds for Semantic Structures](https://mlsanity.com/paper/1808.10485/) (2018)
- [DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling](https://mlsanity.com/paper/2107.01875/) (2021)
- [HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge](https://mlsanity.com/paper/2109.05097/) (2021)
- [Phoneme-BERT: Joint Language Modelling of Phoneme Sequence and ASR Transcript](https://mlsanity.com/paper/2102.00804/) (2021)
- [Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange](https://mlsanity.com/paper/1909.00088/) (2019)
- [Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/1907.04224/) (2019)
- [ALOHA: Artificial Learning of Human Attributes for Dialogue Agents](https://mlsanity.com/paper/1910.08293/) (2019)
- [NAREOR: The Narrative Reordering Problem](https://mlsanity.com/paper/2104.06669/) (2021)
- [Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models](https://mlsanity.com/paper/2109.03892/) (2021)
- [SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation](https://mlsanity.com/paper/2108.06643/) (2021)

## Built on by (most cited first, 6 of 6)

- [TwistList: Resources and Baselines for Tongue Twister Generation](https://mlsanity.com/paper/2306.03457/) (2023)
- [CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models](https://mlsanity.com/paper/2210.04191/) (2022)
- [Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes](https://mlsanity.com/paper/2507.13335/) (2025)
- [Who's Laughing Now? An Overview of Computational Humour Generation and Explanation](https://mlsanity.com/paper/2509.21175/) (2025)
- [Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases](https://mlsanity.com/paper/2403.13901/) (2024)
- [PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation](https://mlsanity.com/paper/2209.07752/) (2022)

## Abstract (arXiv)

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.