# Mitigating the Linguistic Gap with Phonemic Representations for Robust Cross-lingual Transfer

arXiv: https://arxiv.org/abs/2402.14279 · HTML: https://mlsanity.com/paper/2402.14279/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://mlsanity.com/paper/2103.06874/) (2021)
- [Are All Languages Created Equal in Multilingual BERT?](https://mlsanity.com/paper/2005.09093/) (2020)
- [When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models](https://mlsanity.com/paper/2010.12858/) (2020)
- [First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT](https://mlsanity.com/paper/2101.11109/) (2021)
- [PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTS](https://mlsanity.com/paper/2103.15060/) (2021)
- [Enhancing Cross-lingual Transfer by Manifold Mixup](https://mlsanity.com/paper/2205.04182/) (2022)
- [Match the Script, Adapt if Multilingual: Analyzing the Effect of Multilingual Pretraining on Cross-lingual Transferability](https://mlsanity.com/paper/2203.10753/) (2022)
- [Phoneme-Level BERT for Enhanced Prosody of Text-to-Speech with Grapheme Predictions](https://mlsanity.com/paper/2301.08810/) (2023)
- [Phoneme-BERT: Joint Language Modelling of Phoneme Sequence and ASR Transcript](https://mlsanity.com/paper/2102.00804/) (2021)
- [Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations](https://mlsanity.com/paper/1808.09500/) (2018)
- [XPhoneBERT: A Pre-trained Multilingual Model for Phoneme Representations for Text-to-Speech](https://mlsanity.com/paper/2305.19709/) (2023)
- [Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens](https://mlsanity.com/paper/2305.11550/) (2023)
- [Alternative Input Signals Ease Transfer in Multilingual Machine Translation](https://mlsanity.com/paper/2110.07804/) (2021)
- [Mixed-Phoneme BERT: Improving BERT with Mixed Phoneme and Sup-Phoneme Representations for Text to Speech](https://mlsanity.com/paper/2203.17190/) (2022)
- [Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation](https://mlsanity.com/paper/2305.14189/) (2023)
- [Similarity of Sentence Representations in Multilingual LMs: Resolving Conflicting Literature and Case Study of Baltic Languages](https://mlsanity.com/paper/2109.01207/) (2021)

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

- [Understanding Language Prior of LVLMs by Contrasting Chain-of-Embedding](https://mlsanity.com/paper/2509.23050/) (2025)
- [Natural Language Processing RELIES on Linguistics](https://mlsanity.com/paper/2405.05966/) (2024)
- [OLA: Output Language Alignment in Code-Switched LLM Interactions](https://mlsanity.com/paper/2601.03589/) (2026)
- [Happiness is Sharing a Vocabulary: A Study of Transliteration Methods](https://mlsanity.com/paper/2510.10827/) (2025)
- [Harnessing Linguistic Dissimilarity for Language Generalization on Unseen Low-Resource Varieties](https://mlsanity.com/paper/2605.04500/) (2026)
- [One Form to Transfer Them All: Pretraining Multilingual Language Models Beyond Native Orthography](https://mlsanity.com/paper/2608.25904/) (2026)

## Abstract (arXiv)

Approaches to improving multilingual language understanding often struggle with significant performance gaps between high-resource and low-resource languages. While there are efforts to align the languages in a single latent space to mitigate such gaps, how different input-level representations influence such gaps has not been investigated, particularly with phonemic inputs. We hypothesize that the performance gaps are affected by representation discrepancies between these languages, and revisit the use of phonemic representations as a means to mitigate these discrepancies. To demonstrate the effectiveness of phonemic representations, we present experiments on three representative cross-lingual tasks on 12 languages in total. The results show that phonemic representations exhibit higher similarities between languages compared to orthographic representations, and it consistently outperforms grapheme-based baseline model on languages that are relatively low-resourced. We present quantitative evidence from three cross-lingual tasks that demonstrate the effectiveness of phonemic representations, and it is further justified by a theoretical analysis of the cross-lingual performance gap.