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.

Similar Papers

CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving (2025.coling-main)

Copied to clipboard

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.
Tutorial: End-to-End Speech Translation (2021.eacl-tutorials)

Copied to clipboard

Challenge: Speech translation is the translation of speech in one language typically to text in another, traditionally accomplished through a combination of automatic speech recognition and machine translation.
Approach: This tutorial introduces the techniques used in cutting-edge research on speech translation.
Outcome: The proposed models achieve state-of-the-art performance with end-to-end speech translation for both high- and low-resource languages.
Discourse-Driven Code-Switching: Analyzing the Role of Content and Communicative Function in Spanish-English Bilingual Speech (2025.emnlp-main)

Copied to clipboard

Challenge: Prior work has shown that a range of speaker and listener attributes affect or correlate with the prevalence of code-switching during conversation.
Approach: They analyze the names of entities and dialogue acts present in a Spanish-English spontaneous speech corpus and build a predictive model of CSW.
Outcome: The proposed model is the first to take a discourse-sensitive approach to understanding pragmatic and referential cues of bilingual speech.
Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative Training (D19-1)

Copied to clipboard

Challenge: Code-switching (CS) is a linguistic phenomenon defined as "the alternation of two languages within a single discourse, sentence or constituent."
Approach: They propose an ASR-motivated evaluation setup which is decoupled from an ASL system and the choice of vocabulary . they propose a discriminative training approach which works better than generative language modeling .
Outcome: The proposed evaluation setup is better than generative language modeling, the authors show . the proposed setup is decoupled from an ASR system and the choice of vocabulary .
Automatic Identification of Code-Switching Functions in Speech Transcripts (2023.findings-acl)

Copied to clipboard

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.
Analyzing the Role of Part-of-Speech in Code-Switching: A Corpus-Based Study (2024.findings-eacl)

Copied to clipboard

Challenge: Code-switching (CS) is a common linguistic phenomenon wherein speakers fluidly transition between languages in conversation.
Approach: They propose to use a part-of-speech (POS)-based analysis of Spanish-English and Mandarin-English corpora to examine the propensity of bilinguals to engage in CS.
Outcome: The findings confirm the existence of a statistically significant connection between POS and the likelihood of CS across language pairs, but show that it diminishes as tokens distance themselves from CS instances.
Code-Switched Language Identification is Harder Than You Think (2024.eacl-long)

Copied to clipboard

Challenge: Code switching (CS) is a common phenomenon in written and spoken communication, but is handled poorly by many NLP applications.
Approach: They propose to use CS language identification for corpus building to make it more realistic by scaling it to more languages and considering models with simpler architectures for faster inference.
Outcome: The proposed system is based on a sentence-level multi-label tagging problem and provides recommendations for future work.
Speech Translation and the End-to-End Promise: Taking Stock of Where We Are (2020.acl-main)

Copied to clipboard

Challenge: Until recently, the only feasible approach to translating acoustic speech signals into text was the cascaded approach.
Approach: They propose a classification of the main challenges of traditional approaches to speech translation . they argue that end-to-end models fall short due to compromises made to address data scarcity .
Outcome: This paper provides a brief survey of the main challenges of traditional approaches in speech translation . it reveals that many end-to-end models fail due to compromises made to address data scarcity.
UniCoM: A Universal Code-Switching Speech Generator (2025.findings-emnlp)

Copied to clipboard

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.
CoSSAT: Code-Switched Speech Annotation Tool (D19-59)

Copied to clipboard

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.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations