# Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition

arXiv: https://arxiv.org/abs/1909.08504 · HTML: https://mlsanity.com/paper/1909.08504/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [LLaMA Beyond English: An Empirical Study on Language Capability Transfer](https://mlsanity.com/paper/2401.01055/) (2024)
- [Are Multilingual Models Effective in Code-Switching?](https://mlsanity.com/paper/2103.13309/) (2021)
- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing](https://mlsanity.com/paper/2101.10524/) (2021)
- [CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing](https://mlsanity.com/paper/2106.06004/) (2021)
- [A Survey on Word Meta-Embedding Learning](https://mlsanity.com/paper/2204.11660/) (2022)
- [Progressive Sentiment Analysis for Code-Switched Text Data](https://mlsanity.com/paper/2210.14380/) (2022)

## Abstract (arXiv)

In countries that speak multiple main languages, mixing up different languages within a conversation is commonly called code-switching. Previous works addressing this challenge mainly focused on word-level aspects such as word embeddings. However, in many cases, languages share common subwords, especially for closely related languages, but also for languages that are seemingly irrelevant. Therefore, we propose Hierarchical Meta-Embeddings (HME) that learn to combine multiple monolingual word-level and subword-level embeddings to create language-agnostic lexical representations. On the task of Named Entity Recognition for English-Spanish code-switching data, our model achieves the state-of-the-art performance in the multilingual settings. We also show that, in cross-lingual settings, our model not only leverages closely related languages, but also learns from languages with different roots. Finally, we show that combining different subunits are crucial for capturing code-switching entities.