# Subword-Level Language Identification for Intra-Word Code-Switching

arXiv: https://arxiv.org/abs/1904.01989 · HTML: https://mlsanity.com/paper/1904.01989/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 12 of 12)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [End-to-End Speech Translation for Code Switched Speech](https://mlsanity.com/paper/2204.05076/) (2022)
- [Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling](https://mlsanity.com/paper/2103.07792/) (2021)
- [MaskLID: Code-Switching Language Identification through Iterative Masking](https://mlsanity.com/paper/2406.06263/) (2024)
- [Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text](https://mlsanity.com/paper/2210.06990/) (2022)
- [Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction](https://mlsanity.com/paper/2306.06804/) (2023)
- [Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching](https://mlsanity.com/paper/2111.01231/) (2021)
- [Evaluating Modern Large Language Models on Low-Resource and Morphologically Rich Languages:A Cross-Lingual Benchmark Across Cantonese, Japanese, and Turkish](https://mlsanity.com/paper/2511.10664/) (2025)
- [Learning Mutually Informed Representations for Characters and Subwords](https://mlsanity.com/paper/2311.07853/) (2023)
- [Computational Approaches to Arabic-English Code-Switching](https://mlsanity.com/paper/2410.13318/) (2024)
- [LuxBorrow: From Pompier to Pompjee, Tracing Borrowing in Luxembourgish](https://mlsanity.com/paper/2603.10789/) (2026)
- [Improving Language Identification for Code-Switched Utterances with Integer Linear Programming](https://mlsanity.com/paper/2609.05099/) (2026)

## Abstract (arXiv)

Language identification for code-switching (CS), the phenomenon of alternating between two or more languages in conversations, has traditionally been approached under the assumption of a single language per token. However, if at least one language is morphologically rich, a large number of words can be composed of morphemes from more than one language (intra-word CS). In this paper, we extend the language identification task to the subword-level, such that it includes splitting mixed words while tagging each part with a language ID. We further propose a model for this task, which is based on a segmental recurrent neural network. In experiments on a new Spanish--Wixarika dataset and on an adapted German--Turkish dataset, our proposed model performs slightly better than or roughly on par with our best baseline, respectively. Considering only mixed words, however, it strongly outperforms all baselines.