# Universal Dependency Parsing for Hindi-English Code-switching

arXiv: https://arxiv.org/abs/1804.05868 · HTML: https://mlsanity.com/paper/1804.05868/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2018.

## Built on by (most cited first, 18 of 18)

- [L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models](https://mlsanity.com/paper/2204.08398/) (2022)
- [El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing](https://mlsanity.com/paper/2101.10524/) (2021)
- [Genre as Weak Supervision for Cross-lingual Dependency Parsing](https://mlsanity.com/paper/2109.04733/) (2021)
- [From Human Judgements to Predictive Models: Unravelling Acceptability in Code-Mixed Sentences](https://mlsanity.com/paper/2405.05572/) (2024)
- [GupShup: An Annotated Corpus for Abstractive Summarization of Open-Domain Code-Switched Conversations](https://mlsanity.com/paper/2104.08578/) (2021)
- [Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching](https://mlsanity.com/paper/2111.01231/) (2021)
- [CST5: Data Augmentation for Code-Switched Semantic Parsing](https://mlsanity.com/paper/2211.07514/) (2022)
- [Out-of-Domain Evaluation of Finnish Dependency Parsing](https://mlsanity.com/paper/2204.10621/) (2022)
- [Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages](https://mlsanity.com/paper/2506.07274/) (2025)
- [ViLexNorm: A Lexical Normalization Corpus for Vietnamese Social Media Text](https://mlsanity.com/paper/2401.16403/) (2024)
- [Improving Cross-lingual Representation for Semantic Retrieval with Code-switching](https://mlsanity.com/paper/2403.01364/) (2024)
- [A Weakly Supervised Data Labeling Framework for Machine Lexical Normalization in Vietnamese Social Media](https://mlsanity.com/paper/2409.20467/) (2024)
- [Enhancing Multilingual Language Models for Code-Switched Input Data](https://mlsanity.com/paper/2503.07990/) (2025)
- [MultiLexNorm++: A Unified Benchmark and a Generative Model for Lexical Normalization for Asian Languages](https://mlsanity.com/paper/2601.16623/) (2026)
- [MANorm: A Normalization Dictionary for Moroccan Arabic Dialect Written in Latin Script](https://mlsanity.com/paper/2206.09167/) (2022)
- [Part-of-Speech Tagging of Odia Language Using statistical and Deep Learning-Based Approaches](https://mlsanity.com/paper/2207.03256/) (2022)
- [Cordyceps@LT-EDI: Patching Language-Specific Homophobia/Transphobia Classifiers with a Multilingual Understanding](https://mlsanity.com/paper/2309.13561/) (2023)
- [Lost in Speech: Benchmarking, Evaluation, and Parsing of Spoken Bilingual Conversational Language Beyond Standard UD Assumptions](https://mlsanity.com/paper/2602.06307/) (2026)

## Abstract (arXiv)

Code-switching is a phenomenon of mixing grammatical structures of two or more languages under varied social constraints. The code-switching data differ so radically from the benchmark corpora used in NLP community that the application of standard technologies to these data degrades their performance sharply. Unlike standard corpora, these data often need to go through additional processes such as language identification, normalization and/or back-transliteration for their efficient processing. In this paper, we investigate these indispensable processes and other problems associated with syntactic parsing of code-switching data and propose methods to mitigate their effects. In particular, we study dependency parsing of code-switching data of Hindi and English multilingual speakers from Twitter. We present a treebank of Hindi-English code-switching tweets under Universal Dependencies scheme and propose a neural stacking model for parsing that efficiently leverages part-of-speech tag and syntactic tree annotations in the code-switching treebank and the preexisting Hindi and English treebanks. We also present normalization and back-transliteration models with a decoding process tailored for code-switching data. Results show that our neural stacking parser is 1.5% LAS points better than the augmented parsing model and our decoding process improves results by 3.8% LAS points over the first-best normalization and/or back-transliteration.