# Cross-Lingual IPA Contrastive Learning for Zero-Shot NER

arXiv: https://arxiv.org/abs/2503.07214 · HTML: https://mlsanity.com/paper/2503.07214/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 30 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [A Theoretical Analysis of Contrastive Unsupervised Representation Learning](https://mlsanity.com/paper/1902.09229/) (2019)
- [Understanding the Behaviour of Contrastive Loss](https://mlsanity.com/paper/2012.09740/) (2020)
- [Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss](https://mlsanity.com/paper/2106.04156/) (2021)
- [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://mlsanity.com/paper/2103.06874/) (2021)
- [Predicting What You Already Know Helps: Provable Self-Supervised Learning](https://mlsanity.com/paper/2008.01064/) (2020)
- [Contrastive learning, multi-view redundancy, and linear models](https://mlsanity.com/paper/2008.10150/) (2020)
- [Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning](https://mlsanity.com/paper/2105.15134/) (2021)
- [Understanding Contrastive Learning Requires Incorporating Inductive Biases](https://mlsanity.com/paper/2202.14037/) (2022)
- [How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2203.16262/) (2022)
- [Understanding Self-supervised Learning with Dual Deep Networks](https://mlsanity.com/paper/2010.00578/) (2020)
- [ByT5 model for massively multilingual grapheme-to-phoneme conversion](https://mlsanity.com/paper/2204.03067/) (2022)
- [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)
- [mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning](https://mlsanity.com/paper/2308.09073/) (2023)
- [ZGUL: Zero-shot Generalization to Unseen Languages using Multi-source Ensembling of Language Adapters](https://mlsanity.com/paper/2310.16393/) (2023)
- [On the Comparison between Multi-modal and Single-modal Contrastive Learning](https://mlsanity.com/paper/2411.02837/) (2024)
- [Temperature-Free Loss Function for Contrastive Learning](https://mlsanity.com/paper/2501.17683/) (2025)
- [PWESuite: Phonetic Word Embeddings and Tasks They Facilitate](https://mlsanity.com/paper/2304.02541/) (2023)
- [Phoneme-Level Contrastive Learning for User-Defined Keyword Spotting with Flexible Enrollment](https://mlsanity.com/paper/2412.20805/) (2024)
- [IPA-CLIP: Integrating Phonetic Priors into Vision and Language Pretraining](https://mlsanity.com/paper/2303.03144/) (2023)

## Built on by (most cited first, 1 of 1)

- [Phonemes to the Rescue: Multilingual Tokenization Based on International Phonetic Alphabet](https://mlsanity.com/paper/2606.20993/) (2026)

## Abstract (arXiv)

Existing approaches to zero-shot Named Entity Recognition (NER) for low-resource languages have primarily relied on machine translation, whereas more recent methods have shifted focus to phonemic representation. Building upon this, we investigate how reducing the phonemic representation gap in IPA transcription between languages with similar phonetic characteristics enables models trained on high-resource languages to perform effectively on low-resource languages. In this work, we propose CONtrastive Learning with IPA (CONLIPA) dataset containing 10 English and high resource languages IPA pairs from 10 frequently used language families. We also propose a cross-lingual IPA Contrastive learning method (IPAC) using the CONLIPA dataset. Furthermore, our proposed dataset and methodology demonstrate a substantial average gain when compared to the best performing baseline.