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        <title>Improving Language Identification for Code-Switched Utterances with Integer Linear Programming</title>
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        <namePart type="given">Joanna</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Improving Language Identification for Code-Switched Utterances with Integer Linear Programming
%A Radoła, Joanna
%A Crego, Josep
%A Yvon, François
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F radola-etal-2026-improving
%X 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.
%U https://aclanthology.org/2026.findings-emnlp.1429/
%P 30450-30468

Markdown (Informal)

[Improving Language Identification for Code-Switched Utterances with Integer Linear Programming](https://aclanthology.org/2026.findings-emnlp.1429/) (Radoła et al., Findings 2026)

• Improving Language Identification for Code-Switched Utterances with Integer Linear Programming (Radoła et al., Findings 2026)

ACL

• Joanna Radoła, Josep Crego, and François Yvon. 2026. Improving Language Identification for Code-Switched Utterances with Integer Linear Programming. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 30450–30468, Budapest, Hungary. Association for Computational Linguistics.