Code switching (CS) refers to the phenomenon of interchangeably using words
and phrases from different languages. CS can pose significant accuracy
challenges to NLP, due to the often monolingual nature of the underlying
systems. In this work, we focus on CS in the context of English/Spanish
conversations for the task of speech translation (ST), generating and
evaluating both transcript and translation. To evaluate model performance on
this task, we create a novel ST corpus derived from existing public data sets.
We explore various ST architectures across two dimensions: cascaded (transcribe
then translate) vs end-to-end (jointly transcribe and translate) and
unidirectional (source -> target) vs bidirectional (source <-> target). We show
that our ST architectures, and especially our bidirectional end-to-end
architecture, perform well on CS speech, even when no CS training data is used.

本篇研究关注英语 / 西班牙语对话中出现的代码转换，探讨在语音翻译任务中采用级联和端到端、单向和双向等不同架构的模型表现，得出双向端到端模型表现良好的结论。