# Bilingual Language Modeling, A transfer learning technique for Roman Urdu

arXiv: https://arxiv.org/abs/2102.10958 · HTML: https://mlsanity.com/paper/2102.10958/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 91 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 29 of 29)

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- [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://mlsanity.com/paper/1905.07213/) (2019)
- [BERTje: A Dutch BERT Model](https://mlsanity.com/paper/1912.09582/) (2019)
- [WikiBERT models: deep transfer learning for many languages](https://mlsanity.com/paper/2006.01538/) (2020)
- [Multilingual Models for Compositional Distributed Semantics](https://mlsanity.com/paper/1404.4641/) (2014)
- [Bilingual Distributed Word Representations from Document-Aligned Comparable Data](https://mlsanity.com/paper/1509.07308/) (2015)
- [A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments](https://mlsanity.com/paper/1608.05426/) (2016)

## Abstract (arXiv)

Pretrained language models are now of widespread use in Natural Language Processing. Despite their success, applying them to Low Resource languages is still a huge challenge. Although Multilingual models hold great promise, applying them to specific low-resource languages e.g. Roman Urdu can be excessive. In this paper, we show how the code-switching property of languages may be used to perform cross-lingual transfer learning from a corresponding high resource language. We also show how this transfer learning technique termed Bilingual Language Modeling can be used to produce better performing models for Roman Urdu. To enable training and experimentation, we also present a collection of novel corpora for Roman Urdu extracted from various sources and social networking sites, e.g. Twitter. We train Monolingual, Multilingual, and Bilingual models of Roman Urdu - the proposed bilingual model achieves 23% accuracy compared to the 2% and 11% of the monolingual and multilingual models respectively in the Masked Language Modeling (MLM) task.