# Script-Agnostic Language Identification

arXiv: https://arxiv.org/abs/2406.17901 · HTML: https://mlsanity.com/paper/2406.17901/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models](https://mlsanity.com/paper/2010.12858/) (2020)
- [UNKs Everywhere: Adapting Multilingual Language Models to New Scripts](https://mlsanity.com/paper/2012.15562/) (2020)
- [Automatic Language Identification in Texts: A Survey](https://mlsanity.com/paper/1804.08186/) (2018)
- [Pushing the Limits of Low-Resource Morphological Inflection](https://mlsanity.com/paper/1908.05838/) (2019)
- [Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages](https://mlsanity.com/paper/2109.10534/) (2021)
- [AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages](https://mlsanity.com/paper/2305.06897/) (2023)
- [Does Transliteration Help Multilingual Language Modeling?](https://mlsanity.com/paper/2201.12501/) (2022)
- [PALI: A Language Identification Benchmark for Perso-Arabic Scripts](https://mlsanity.com/paper/2304.01322/) (2023)
- [LIMIT: Language Identification, Misidentification, and Translation using Hierarchical Models in 350+ Languages](https://mlsanity.com/paper/2305.14263/) (2023)

## Abstract (arXiv)

Language identification is used as the first step in many data collection and crawling efforts because it allows us to sort online text into language-specific buckets. However, many modern languages, such as Konkani, Kashmiri, Punjabi etc., are synchronically written in several scripts. Moreover, languages with different writing systems do not share significant lexical, semantic, and syntactic properties in neural representation spaces, which is a disadvantage for closely related languages and low-resource languages, especially those from the Indian Subcontinent. To counter this, we propose learning script-agnostic representations using several different experimental strategies (upscaling, flattening, and script mixing) focusing on four major Dravidian languages (Tamil, Telugu, Kannada, and Malayalam). We find that word-level script randomization and exposure to a language written in multiple scripts is extremely valuable for downstream script-agnostic language identification, while also maintaining competitive performance on naturally occurring text.