| Challenge: | Code-switching is a phenomenon that occurs in multilingual societies where speakers who are fluent in two or more languages switch between these languages in the same conversation or utterance. |
| Approach: | They propose an interface which helps annotators transcribe code-switched speech faster, more easily and more accurately than a traditional interface. |
| Outcome: | The proposed interface can be used by 10 users to transcribe Hindi-English code-switched speech faster, easier and more accurately than a traditional interface. |
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Automatic Identification of Code-Switching Functions in Speech Transcripts (2023.findings-acl)
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| Challenge: | Code-switching, or switching between languages, occurs for many reasons and has important linguistic, sociological, and cultural implications. |
| Approach: | They build a system to identify a wide range of functions for which speakers code-switch in everyday speech with an accuracy of 75% . they use a dataset of Hindi-English code-witched data to analyze their results . |
| Outcome: | The proposed system can identify a wide range of functions for which speakers code-switch in everyday speech, with an accuracy of 75% across all functions. |
PolyWER: A Holistic Evaluation Framework for Code-Switched Speech Recognition (2024.findings-emnlp)
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| Challenge: | Existing methods for measuring accuracy, such as Word Error Rate (WER), are too strict to address this challenge. |
| Approach: | They propose a framework for evaluating speech recognition systems to handle language-mixing by appending annotations to a publicly available Arabic-English code-switched dataset. |
| Outcome: | The proposed framework evaluates speech recognition systems against human judgement and a publicly available Arabic-English code-switched dataset. |
End-to-End Speech Translation for Code Switched Speech (2022.findings-acl)
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Orion Weller, Matthias Sperber, Telmo Pires, Hendra Setiawan, Christian Gollan, Dominic Telaar, Matthias Paulik
| Challenge: | Code switching (CS) is the phenomenon of interchangeably using words and phrases from different languages. |
| Approach: | They propose a new ST corpus that extends the joint transcription and translation setup. |
| Outcome: | The proposed model performs well even when no training data is used. |
CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving (2025.coling-main)
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| Challenge: | More than half of the world's population is presumed to be bilingual . spoken translation of code-switched speech has been under-explored . |
| Approach: | They propose an end-to-end model architecture CoSTA that scaffolds on pretrained ASR and MT modules. |
| Outcome: | The proposed model outperforms existing models by 3.5 BLEU points in spoken translation of code-switched speech. |
Collection and Analysis of Code-switch Egyptian Arabic-English Speech Corpus (L18-1)
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| Challenge: | despite of the great demand, there is still a huge shortage in available corpora for dialectal languages and code-switched speech. |
| Approach: | They collect conversational Egyptian Arabic spontaneous speech, extract transcriptions and analyze it from a code-switching perspective. |
| Outcome: | The authors collect conversational Egyptian Arabic spontaneous speech, extract transcriptions and analyze speech from the code-switching perspective. |
CoVoSwitch: Machine Translation of Synthetic Code-Switched Text Based on Intonation Units (2024.acl-srw)
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| Challenge: | Multilingual code-switching research is often hindered by the lack and linguistically biased status of available datasets. |
| Approach: | They synthesize code-switching data by replacing intonation units detected through PSST, a speech segmentation model fine-tuned from OpenAI’s Whisper, using a language-to-text translation dataset, CoVoST 2. |
| Outcome: | The proposed model outperforms two monolingual models and is better at code-switching translation into English than non-English. |
CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus (2020.lrec-1)
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| Challenge: | Existing datasets involve language pairs with English as source language, are low resource or lack labeled data. |
| Approach: | They propose a multilingual speech-to-text translation corpus from 11 languages into English . they provide empirical evidence of the quality of the data and provide initial benchmarks . |
| Outcome: | The proposed model is the first end-to-end multilingual model for spoken language translation. |
Part-of-Speech Tagging for Code-Switched, Transliterated Texts without Explicit Language Identification (D18-1)
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| Challenge: | Code-switching is a challenge for NLP due to the lack of representative data for training models. |
| Approach: | They propose a model that is trained exclusively on monolingual resources but can be applied to unseen code-switched text at inference time. |
| Outcome: | The proposed model outperforms standard models on Hindi-English part-of-speech tagging and on unannotated code-switched text with alternate scripts. |
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. |
| Approach: | They propose a neural stacking model that leverages part-of-speech tags and syntactic tree annotations in tweets to parse code-switching data. |
| Outcome: | The proposed model is 1.5% better than the augmented model and 3.8% better than one which uses first-best normalization and/or back-transliteration. |
UniCoM: A Universal Code-Switching Speech Generator (2025.findings-emnlp)
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| Challenge: | Code-switching (CS) is a common phenomenon in real-world conversations and poses significant challenges for multilingual speech technology. |
| Approach: | They propose a pipeline for generating high-quality, natural CS samples without altering sentence semantics. |
| Outcome: | The proposed pipeline generates high-quality, natural CS samples without altering sentence semantics without alteration of sentence semantic. |