This paper was accepted at the EMNLP Workshop on Computational Approaches to Linguistic Code-Switching (CALCS). It explores the challenges of code-switching (mixing different languages in a sentence) in Natural Language Processing (NLP). Previous studies have shown promising results for end-to-end speech translation, but this paper focuses on unexplored areas.
Towards Real-World Streaming Speech Translation for Code-Switched Speech
In this paper, we present a groundbreaking approach to addressing the challenges of code-switching in communication. Code-switching, which involves mixing different languages in a single sentence, is a common phenomenon that can be difficult to handle in Natural Language Processing (NLP) settings.
Past studies have shown promising results for end-to-end speech translation (ST) in code-switched speech, but they have been limited to offline scenarios and translating to only one of the languages present in the source.
Our research focuses on two crucial yet unexplored areas…
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