  We present MaskLID, a simple, yet effective, code-switching (CS) language
identification (LID) method. MaskLID does not require any training and is
designed to complement current high-performance sentence-level LIDs.
Sentence-level LIDs are classifiers trained on monolingual texts to provide
single labels, typically using a softmax layer to turn scores into
probabilities. However, in cases where a sentence is composed in both L1 and L2
languages, the LID classifier often only returns the dominant label L1. To
address this limitation, MaskLID employs a strategy to mask text features
associated with L1, allowing the LID to classify the text as L2 in the next
round. This method uses the LID itself to identify the features that require
masking and does not rely on any external resource. In this work, we explore
the use of MaskLID for two open-source LIDs (GlotLID and OpenLID), that are
both based on the FastText architecture. Code and demo are available at
https://github.com/cisnlp/MaskLID.
