# Can You Traducir This? Machine Translation for Code-Switched Input

arXiv: https://arxiv.org/abs/2105.04846 · HTML: https://mlsanity.com/paper/2105.04846/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 58 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://mlsanity.com/paper/1905.02450/) (2019)
- [Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation](https://mlsanity.com/paper/1804.06609/) (2018)
- [Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism](https://mlsanity.com/paper/1601.01073/) (2016)
- [GLUECoS : An Evaluation Benchmark for Code-Switched NLP](https://mlsanity.com/paper/2004.12376/) (2020)
- [LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation](https://mlsanity.com/paper/2005.04322/) (2020)
- [A Survey of Code-switched Speech and Language Processing](https://mlsanity.com/paper/1904.00784/) (2019)
- [Training Neural Machine Translation To Apply Terminology Constraints](https://mlsanity.com/paper/1906.01105/) (2019)
- [SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets](https://mlsanity.com/paper/2008.04277/) (2020)
- [Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences](https://mlsanity.com/paper/1909.08582/) (2019)
- [Code-Switching for Enhancing NMT with Pre-Specified Translation](https://mlsanity.com/paper/1904.09107/) (2019)
- [Code-switching pre-training for neural machine translation](https://mlsanity.com/paper/2009.08088/) (2020)
- [Overview for the Second Shared Task on Language Identification in Code-Switched Data](https://mlsanity.com/paper/1909.13016/) (2019)
- [Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task](https://mlsanity.com/paper/1906.04138/) (2019)
- [From English to Code-Switching: Transfer Learning with Strong Morphological Clues](https://mlsanity.com/paper/1909.05158/) (2019)
- [Code-switched Language Models Using Dual RNNs and Same-Source Pretraining](https://mlsanity.com/paper/1809.01962/) (2018)
- [Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative Training](https://mlsanity.com/paper/1810.11895/) (2018)

## Built on by (most cited first, 21 of 21)

- [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)
- [ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic - English](https://mlsanity.com/paper/2211.12000/) (2022)
- [CALCS 2021 Shared Task: Machine Translation for Code-Switched Data](https://mlsanity.com/paper/2202.09625/) (2022)
- [Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation](https://mlsanity.com/paper/2205.12649/) (2022)
- [Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text](https://mlsanity.com/paper/2210.06990/) (2022)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [Data-adaptive Transfer Learning for Translation: A Case Study in Haitian and Jamaican](https://mlsanity.com/paper/2209.06295/) (2022)
- [Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Study](https://mlsanity.com/paper/2310.15262/) (2023)
- [Conditioning LLMs to Generate Code-Switched Text](https://mlsanity.com/paper/2502.12924/) (2025)
- [Low-resource Machine Translation for Code-switched Kazakh-Russian Language Pair](https://mlsanity.com/paper/2503.20007/) (2025)
- [One Source, Two Targets: Challenges and Rewards of Dual Decoding](https://mlsanity.com/paper/2109.10197/) (2021)
- [Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning](https://mlsanity.com/paper/2305.19759/) (2023)
- [Grammatical Error Correction for Code-Switched Sentences by Learners of English](https://mlsanity.com/paper/2404.12489/) (2024)
- [On Creating an English-Thai Code-switched Machine Translation in Medical Domain](https://mlsanity.com/paper/2410.16221/) (2024)
- [Automatic Identification of Motivation for Code-Switching in Speech Transcripts](https://mlsanity.com/paper/2212.08565/) (2022)
- [The Effect of Alignment Objectives on Code-Switching Translation](https://mlsanity.com/paper/2309.05044/) (2023)
- [CoVoSwitch: Machine Translation of Synthetic Code-Switched Text Based on Intonation Units](https://mlsanity.com/paper/2407.14295/) (2024)
- [The Impact of Code-switched Synthetic Data Quality is Task Dependent: Insights from MT and ASR](https://mlsanity.com/paper/2503.23576/) (2025)
- [Towards Fine-Grained Code-Switch Speech Translation with Semantic Space Alignment](https://mlsanity.com/paper/2511.10670/) (2025)
- [Domain Curricula for Code-Switched MT at MixMT 2022](https://mlsanity.com/paper/2210.17463/) (2022)

## Abstract (arXiv)

Code-Switching (CSW) is a common phenomenon that occurs in multilingual geographic or social contexts, which raises challenging problems for natural language processing tools. We focus here on Machine Translation (MT) of CSW texts, where we aim to simultaneously disentangle and translate the two mixed languages. Due to the lack of actual translated CSW data, we generate artificial training data from regular parallel texts. Experiments show this training strategy yields MT systems that surpass multilingual systems for code-switched texts. These results are confirmed in an alternative task aimed at providing contextual translations for a L2 writing assistant.