CoCoa: An Encoder-Decoder Model for Controllable Code-switched Generation (2022.emnlp-main)
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| Challenge: | Generating code-switched text with fine-grained control on the degree of code-witching and the lexical choices used to convey formality has been well-explored. |
| Approach: | They propose to generate code-switched text with fine-grained control on the degree of code-changing and lexical choices used to convey formality. |
| Outcome: | The proposed model can be invoked at test-time to synthesize code-switched text faithful to syntactic and lexical attributes relevant to code-witching. |
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| Challenge: | a computational model for code-switching text is lacking in the corpus of real text. |
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A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning (2020.findings-emnlp)
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| Challenge: | Existing methods to train neural network-based models for code-mixing are limited due to language specificity of code-mixed text. |
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Code-Switched Text Synthesis in Unseen Language Pairs (2023.findings-acl)
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| Challenge: | Existing efforts on text synthesis for code-switching require training on code-witched texts in the target language pairs. |
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CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation (2021.emnlp-main)
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Universal Dependency Parsing for Hindi-English Code-Switching (N18-1)
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| Challenge: | Code-switching data often need additional processes such as language identification, normalization and/or back-transliteration to be processed. |
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| Challenge: | Existing sentiment detection methods are trained on sentiment-labeled monolingual text. |
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Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative Training (D19-1)
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| Challenge: | Code-switching (CS) is a linguistic phenomenon defined as "the alternation of two languages within a single discourse, sentence or constituent." |
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| Challenge: | Existing multilingual understanding models are not capable of generating high-quality text compared with decoder-based causal language models. |
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Code-Mixed Probes Show How Pre-Trained Models Generalise on Code-Switched Text (2024.lrec-main)
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| Challenge: | Code-switching is a prevalent linguistic phenomenon in which multilingual individuals seamlessly alternate between languages. |
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Beyond Monolingual Assumptions: A Survey on Code-Switched NLP in the Era of Large Language Models across Modalities (2026.acl-long)
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| Challenge: | Amidst the rapid advances of large language models, most LLMs struggle with mixed-language inputs, limited Code-switching datasets, and evaluation biases. |
| Approach: | They propose a roadmap for inclusive datasets, fair evaluation, and linguistically grounded models to achieve truly multilingual intelligence. |
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