PhoniTale: Phonologically Grounded Mnemonic Generation for Typologically Distant Language Pairs

• URL: http://arxiv.org/abs/2507.05444v1
• Date: Mon, 07 Jul 2025 19:50:12 GMT
• Title: PhoniTale: Phonologically Grounded Mnemonic Generation for Typologically Distant Language Pairs
• Authors: Sana Kang, Myeongseok Gwon, Su Young Kwon, Jaewook Lee, Andrew Lan, Bhiksha Raj, Rita Singh,
• Abstract summary: Large language models (LLMs) have been used to generate keyword mnemonics by leveraging similar keywords from a learner's first language.<n>We present PhoniTale, a novel cross-lingual mnemonic generation system that retrieves L1 keyword sequence based on phonological similarity.<n>Our findings show that PhoniTale performs comparably to human-authored mnemonics.
• Score: 27.660748686041963
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Vocabulary acquisition poses a significant challenge for second-language (L2) learners, especially when learning typologically distant languages such as English and Korean, where phonological and structural mismatches complicate vocabulary learning. Recently, large language models (LLMs) have been used to generate keyword mnemonics by leveraging similar keywords from a learner's first language (L1) to aid in acquiring L2 vocabulary. However, most of this research has focused on native English speakers learning other languages, rather than the reverse. In this paper, we present PhoniTale, a novel cross-lingual mnemonic generation system that retrieves L1 keyword sequence based on phonological similarity and uses LLMs to generate mnemonics. We evaluate PhoniTale using both automated metrics and human evaluations, comparing its output to mnemonics created by humans and by previous automated approaches. To assess practical effectiveness, we also conduct a short-term recall test measuring mnemonic helpfulness. Our findings show that PhoniTale performs comparably to human-authored mnemonics. We also highlight key areas for future improvement in mnemonic quality and methodology.

Related papers

• Are BabyLMs Second Language Learners? [48.85680614529188]
  This paper describes a linguistically-motivated approach to the 2024 edition of the BabyLM Challenge. Rather than pursuing a first language learning (L1) paradigm, we approach the challenge from a second language (L2) learning perspective.
  arXiv  Detail & Related papers  (2024-10-28T17:52:15Z)
• Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank [4.383205675898942]
  Keywords mnemonics are a technique for memorizing vocabulary through memorable associations with a target word via a verbal cue. We propose a novel overgenerate-and-rank method via prompting large language models to generate verbal cues. Results show that LLM-generated mnemonics are comparable to human-generated ones in terms of imageability, coherence, and perceived usefulness.
  arXiv  Detail & Related papers  (2024-09-21T00:00:18Z)
• PhonologyBench: Evaluating Phonological Skills of Large Language Models [57.80997670335227]
  Phonology, the study of speech's structure and pronunciation rules, is a critical yet often overlooked component in Large Language Model (LLM) research. We present PhonologyBench, a novel benchmark consisting of three diagnostic tasks designed to explicitly test the phonological skills of LLMs. We observe a significant gap of 17% and 45% on Rhyme Word Generation and Syllable counting, respectively, when compared to humans.
  arXiv  Detail & Related papers  (2024-04-03T04:53:14Z)
• Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili [29.252250069388687]
  Tokenization allows for the words to be split based on characters or subwords, creating word embeddings that best represent the structure of the language. We propose a syllable tokenizer and adopt an experiment-centric approach to validate the proposed tokenizer based on the Swahili language.
  arXiv  Detail & Related papers  (2024-03-26T17:26:50Z)
• Information-Theoretic Characterization of Vowel Harmony: A Cross-Linguistic Study on Word Lists [18.138642719651994]
  We define an information-theoretic measure of harmonicity based on predictability of vowels in a natural language lexicon. We estimate this harmonicity using phoneme-level language models (PLMs) Our work demonstrates that word lists are a valuable resource for typological research.
  arXiv  Detail & Related papers  (2023-08-09T11:32:16Z)
• SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues [2.8047215329139976]
  We propose an end-to-end pipeline for auto-generating verbal and visual cues for keyword mnemonics. Our approach, an end-to-end pipeline for auto-generating verbal and visual cues, can automatically generate highly memorable cues.
  arXiv  Detail & Related papers  (2023-05-11T20:58:10Z)
• Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training [66.64843711515341]
  Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text. We call attention to a new setting named multilingual keyphrase generation. We propose a retrieval-augmented method for multilingual keyphrase generation to mitigate the data shortage problem in non-English languages.
  arXiv  Detail & Related papers  (2022-05-21T00:45:21Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)
• Perception Point: Identifying Critical Learning Periods in Speech for Bilingual Networks [58.24134321728942]
  We compare and identify cognitive aspects on deep neural-based visual lip-reading models. We observe a strong correlation between these theories in cognitive psychology and our unique modeling.
  arXiv  Detail & Related papers  (2021-10-13T05:30:50Z)
• Applying Phonological Features in Multilingual Text-To-Speech [2.567123525861164]
  We present a mapping of ARPABET/pinyin to SAMPA/SAMPA-SC and then to phonological features. We tested whether this mapping could lead to the successful generation of native, non-native, and code-switched speech in the two languages.
  arXiv  Detail & Related papers  (2021-10-07T16:37:01Z)
• Automatically Identifying Language Family from Acoustic Examples in Low Resource Scenarios [48.57072884674938]
  We propose a method to analyze language similarity using deep learning. Namely, we train a model on the Wilderness dataset and investigate how its latent space compares with classical language family findings.
  arXiv  Detail & Related papers  (2020-12-01T22:44:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.