PhoniTale: Phonologically Grounded Mnemonic Generation for Typologically Distant Language Pairs

TL;DR

PhoniTale is a novel system that generates phonologically grounded mnemonics for distant language pairs, improving vocabulary learning by combining IPA-based adaptation, syllable alignment, and large language models.

Contribution

It introduces a new phonologically guided approach for automated mnemonic generation, outperforming prior methods and matching human mnemonic quality.

Findings

Outperforms previous automated mnemonic methods.

Achieves mnemonic quality comparable to human-generated cues.

Enhances vocabulary recall in typologically distant language learning.

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 methods still rely on direct IPA-based phonetic matching or employ LLMs without phonological guidance. In this paper, we present PhoniTale, a novel cross-lingual mnemonic generation system that performs IPA-based phonological adaptation and syllable-aware alignment to retrieve L1 keyword sequence and uses LLMs to generate verbal cues. We evaluate PhoniTale through automated metrics and a short-term recall test with human participants, comparing its output to human-written and prior automated mnemonics. Our findings show that PhoniTale consistently outperforms previous automated approaches and achieves quality comparable to human-written mnemonics.