# Joining Hands: Exploiting Monolingual Treebanks for Parsing of Code-mixing Data

arXiv: https://arxiv.org/abs/1703.10772 · HTML: https://mlsanity.com/paper/1703.10772/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2017.

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

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [Representativeness as a Forgotten Lesson for Multilingual and Code-switched Data Collection and Preparation](https://mlsanity.com/paper/2310.20470/) (2023)
- [El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing](https://mlsanity.com/paper/2101.10524/) (2021)
- [On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions](https://mlsanity.com/paper/2103.01273/) (2021)
- [Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Data](https://mlsanity.com/paper/2102.11152/) (2021)
- [SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models](https://mlsanity.com/paper/2210.11670/) (2022)
- [MUTEX: Leveraging Multilingual Transformers and Conditional Random Fields for Enhanced Urdu Toxic Span Detection](https://mlsanity.com/paper/2603.05057/) (2026)

## Abstract (arXiv)

In this paper, we propose efficient and less resource-intensive strategies for parsing of code-mixed data. These strategies are not constrained by in-domain annotations, rather they leverage pre-existing monolingual annotated resources for training. We show that these methods can produce significantly better results as compared to an informed baseline. Besides, we also present a data set of 450 Hindi and English code-mixed tweets of Hindi multilingual speakers for evaluation. The data set is manually annotated with Universal Dependencies.