Improving Language Identification for Code-Switched Utterances with Integer Linear Programming

• URL: http://arxiv.org/abs/2609.05099v1
• Date: Fri, 04 Sep 2026 13:30:01 GMT
• Title: Improving Language Identification for Code-Switched Utterances with Integer Linear Programming
• Abstract summary: This paper revisits MaskLID, a state-of-the art approach for code-switched (CS) identification.<n>MaskLID requires no training and detects arbitrary language combinations.<n>We make three main contributions: (a) we reveal, and address, a major issue of MaskLID; (b) we reformulate the underlying optimization algorithm as an Linear Program; (c) each of these improvements vastly improves the baseline system.
• Score: 19.883639598026253
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Automatic identification of code-switched (CS) utterances remains a challenge for language identification (LID) systems, causing such texts to be underrepresented in the training data of Large Language Models. In this paper, we revisit MaskLID, a state-of-the art approach for CS identification, which requires no training and detects arbitrary language combinations. We make three main contributions: (a) we reveal, and address, a major issue of MaskLID: its overreliance on word-level language association scores; (b) we reformulate the underlying optimization algorithm as an Integer Linear Program, enabling us to experiment with a large set of clear and interpretable constraints; (c) each of these improvements vastly improves the baseline system, as we illustrate in experiments involving 10~diverse languages, where we observe a strong boost in performance on CS benchmarks. We release our code and data for reproducibility.

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