Natural Language Processing (NLP) metrics for bilingual code-switching (CS) have, until now, used words as the token level. However, the assumption that any two words constitute an equally likely switch point is erroneous. In spoken language, a major delimiter of CS is a prosodic chunk known as the Intonation Unit (IU). Switch points are far more likely between words at IU boundaries than between words in the same IU. The word as an elementary NLP unit is thus incommensurate with bilingual speech patterns. Here, we put forward an IU-based adaptation of a familiar metric of CS probability. We then compare the token levels on this metric for ten bilingual datasets featuring multi-word CS. Our comparison shows that the currently standard two-significant-figure precision of the word-based metric is insufficient, as the token level compresses the range of values by inflating the universe of CS. More discerning CS probability values can be obtained by normalizing word-based counts using mean IU length.

Code-switching (CS) metrics in NLP that are based on word-level units are misaligned with true bilingual CS behavior. Crucially, CS is not equally likely between any two words, but follows syntactic and prosodic rules. We adapt two metrics, multilinguality and CS probability, and apply them to transcribed bilingual speech, for the first time putting forward Intonation Units (IUs) – prosodic speech segments – as basic tokens for NLP tasks. In addition, we calculate these two metrics separately for distinct mixing types: alternating-language multi-word strings and single-word incorporations from one language into another. Results indicate that individual differences according to the two CS metrics are independent. However, there is a shared tendency among bilinguals for multi-word CS to occur across, rather than within, IU boundaries. That is, bilinguals tend to prosodically separate their two languages. This constraint is blurred when metric calculations do not distinguish multi-word and single-word items. These results call for a reconsideration of units of analysis in future development of CS datasets for NLP tasks.