Papers with LID
SyMCoM - Syntactic Measure of Code Mixing A Study Of English-Hindi Code-Mixing (2022.findings-acl)
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| Challenge: | Recent work on code mixing in computational settings has leveraged social media code mixed texts to train NLP models. |
| Approach: | They propose to use language ID tags to measure syntactic variety in code-mixed text and their relationship with computational model performance. |
| Outcome: | The proposed measure can be applied to English(en)-hindi(hi) code-mixed datasets and compares them with other measures. |
MaskLID: Code-Switching Language Identification through Iterative Masking (2024.acl-short)
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| Challenge: | Sentence-level LIDs are classifiers trained on monolingual texts to provide single labels, typically using a softmax layer to turn scores into probabilities. |
| Approach: | They propose a simple yet effective code-switching language identification method that uses the LID itself to mask features associated with L1 and L2 in the next round. |
| Outcome: | The proposed method is based on two open-source LIDs based in the FastText architecture and does not require any external resources. |
Bhasa-Abhijnaanam: Native-script and romanized Language Identification for 22 Indic languages (2023.acl-short)
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| Challenge: | Existing tools for language identification are noisy, small and similar to high-resource languages. |
| Approach: | They create a language identification test set for native-script and romanized text which spans all 22 Indic languages and train a model for romanized script. |
| Outcome: | The proposed model improves on native-script and romanized script, and is competitive or better than existing LIDs. |
An Open Dataset and Model for Language Identification (2023.acl-short)
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| Challenge: | Existing LID systems perform poorly on low-resource languages, causing 'representation washing', where the community is given a false view of the actual progress of low-source NLP. |
| Approach: | They propose a model which achieves a macro-average F1 score of 0.93 and a false positive rate of 0.033% across 201 languages, outperforming previous work. |
| Outcome: | The proposed model outperforms existing models and datasets on 201 languages and a false positive rate of 0.033%. |
Improving Informally Romanized Language Identification (2025.emnlp-main)
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| Challenge: | Latin script is often used to informally write languages with non-Latin native scripts, resulting in high spelling variability. |
| Approach: | They propose to improve methods used to synthesize training sets to incorporate natural spelling variations into training sets. |
| Outcome: | The proposed method improves test F1 from the reported 74.7% (using a pretrained neural model) to 85.4% (using the linear classifier trained solely on synthetic data). |
AfroLID: A Neural Language Identification Tool for African Languages (2022.emnlp-main)
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| Challenge: | AfroLID is a neural LID toolkit for 517 African languages and varieties. |
| Approach: | They propose to exploit a multi-domain web dataset manually curated from across 14 language families utilizing five orthographic systems to exploit AfroLID. |
| Outcome: | The proposed tool outperforms existing tools on the acutely under-served Twitter domain. |
Improving Language Identification for Code-Switched Speech: The Pivotal Role of Accented English (2026.findings-eacl)
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| Challenge: | Existing models fail to identify English spoken with the accent of the matrix (dominant) language. |
| Approach: | They propose to fine tune existing LID models with accented English to improve code-switched LID . they use a metric that captures relative ranking of identified languages often overlooked by traditional metrics. |
| Outcome: | The proposed model can be fine tuned with small amounts of accented English without degrading performance on monolingual speech. |
Fumbling in Babel: An Investigation into ChatGPT’s Language Identification Ability (2024.findings-naacl)
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| Challenge: | ChatGPT is a powerful NLP tool but its language identification abilities are unclear. |
| Approach: | They compile a benchmark comprising 670 languages representing 23 language families spoken in five continents and compare their language identification abilities to ChatGPT's (both GPT-3.5 and GPT-4) performance. |
| Outcome: | The proposed model performs poorly on African languages, while GPT-3.5 and GPT-4 perform poorly on English, Afrikaans, Arabic, Indonesian, Italian, Mandarin Chinese, and several more. |
Where are you from? Geolocating Speech and Applications to Language Identification (2024.naacl-long)
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Patrick Foley, Matthew Wiesner, Bismarck Odoom, Leibny Paola Garcia Perera, Kenton Murray, Philipp Koehn
| Challenge: | Language identification (LID) is a critical component in many modern multilingual speech technologies. |
| Approach: | They propose to use radio broadcasts with known origin to train regression models . they also propose to explore using geolocation as a proxy task for LID . |
| Outcome: | The proposed model outperforms pretrained models on the FLEURS benchmark and on the VoxLingua benchmark. |
Unsupervised Preference-Aware Language Identification (2022.findings-acl)
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| Challenge: | Existing studies do not consider inter-personal variations due to the lack of user annotated training data. |
| Approach: | They propose to use user preferences to identify ambiguous texts in multilingual applications without user annotated training data to build a preference-aware LID model. |
| Outcome: | The proposed model significantly outperforms existing LID systems on handling ambiguous texts. |
ConLID: Supervised Contrastive Learning for Low-Resource Language Identification (2026.eacl-long)
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| Challenge: | Low-resource languages and dialects remain difficult to identify and categorize accurately due to data in these languages and are limited to single-domain data. |
| Approach: | They propose a supervised contrastive learning approach to learn domain-invariant representations for low-resource languages by 3.2 percentage points while maintaining its performance for the high-resourced languages. |
| Outcome: | The proposed approach improves LID performance on out-of-domain data for low-resource languages by 3.2 percentage points while maintaining its performance for the high-resourced languages. |
Methods of Automatic Matrix Language Determination for Code-Switched Speech (2024.emnlp-main)
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| Challenge: | Code-switching (CS) is the process of speakers switching between two or more languages in spoken or written language. |
| Approach: | They propose to use the Matrix Language Frame theory to describe CS speech . they compare MLID of English/Mandarin and English/Spanish CS to acoustic language identity . |
| Outcome: | The proposed models outperform monolingual models in acoustic language identity recognition tasks. |
Visibility as Survival: Generalizing NLP for Native Alaskan Language Identification (2025.findings-acl)
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| Challenge: | Indigenous languages are largely invisible in commercial language identification systems, a stark reality exemplified by Google Translate’s LangID tool, which excludes all 150 Indigenous languages of North America. |
| Approach: | They propose a framework that shows how large language models and specialized classifiers can effectively identify these languages with minimal data. |
| Outcome: | The proposed framework shows that large language models and specialized classifiers can effectively identify these languages with minimal data. |
GlotLID: Language Identification for Low-Resource Languages (2023.findings-emnlp)
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| Challenge: | Existing web-mined datasets for low-resource languages have been useful for low resource NLP. |
| Approach: | They propose a model that identifies 1665 low-resource languages and a new model that is rigorously evaluated and reliable. |
| Outcome: | The proposed model outperforms baselines when balancing F1 and false positive rate (FPR). |
OWSM-CTC: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identification (2024.acl-long)
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| Challenge: | Autoregressive models can be slower during inference and have potential risks of hallucination. |
| Approach: | They propose an encoder-only speech foundation model based on Connectionist Temporal Classification. |
| Outcome: | The proposed model improves on 180k hours of public audio data for multilingual speech recognition, speech translation, and language identification. |
CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data (2026.acl-long)
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Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett, Rafael Mosquera, Sara Hincapié Monsalve, Thom Vaughan, Damian Stewart, Malte Ostendorff, Idris Abdulmumin, Vukosi Marivate, Shamsuddeen Hassan Muhammad, Atnafu Lambebo Tonja, Hend Al-Khalifa, Nadia Ghezaiel Hammouda, Verrah Akinyi Otiende, Tack Hwa Wong, Jakhongir Saydaliev, Melika Nobakhtian, Muhammad Ravi Shulthan Habibi, Chalamalasetti Kranti, Carol Muchemi, Khang Nguyen, Faisal Muhammad Adam, Luis Frentzen Salim, Reem Alqifari, Cynthia Jayne Amol, Joseph Marvin Imperial, Ilker Kesen, Ahmad Mustafid, Pavel Stepachev, Leshem Choshen, David Anugraha, Hamada Nayel, Seid Muhie Yimam, Vallerie Alexandra Putra, My Chiffon Nguyen, Azmine Toushik Wasi, Gouthami Vadithya, Rob Van Der Goot, Lanwenn ar C’horr, Karan Dua, Andrew Yates, Mithil Bangera, Yeshil Bangera, Hitesh Laxmichand Patel, Shu Okabe, Fenal Ashokbhai Ilasariya, Dmitry Gaynullin, Genta Indra Winata, Yiyuan Li, Juan Pablo Martínez, Amit Agarwal, Ikhlasul Akmal Hanif, Raia Abu Ahmad, Esther Adenuga, Filbert Aurelian Tjiaranata, Weerayut Buaphet, Michael Anugraha, Sowmya Vajjala, Benjamin L Rice, Azril Hafizi Amirudin, Jesujoba Oluwadara Alabi, Srikant Panda, Yassine Toughrai, Bruhan Kyomuhendo, Daniel Ruffinelli, null Akshata, Manuel Goulão, Ej Zhou, Ingrid Gabriela Franco Ramirez, Cristina Aggazzotti, Konstantin Dobler, Jun Kevin, Quentin Pagès, Nicholas Andrews, Nuhu Ibrahim, Mattes Ruckdeschel, Amr Keleg, Mike Zhang, Casper Rufaro Muziri, Saron Samuel, Sotaro Takeshita, Kun Kerdthaisong, Luca Foppiano, Rasul Dent, Tommaso Green, Ahmad Mustapha Wali, Kamohelo Makaaka, Vicky Feliren, Inshirah Idris, Hande Celikkanat, Abdulhamid Abubakar, Jean Maillard, Benoît Sagot, Thibault Clérice, Kenton Murray, Sarah K. K. Luger
| Challenge: | Language identification (LID) is a fundamental step in curating multilingual corpora. |
| Approach: | They introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages. |
| Outcome: | The proposed benchmark covers 109 languages and shows that existing evaluations overestimate accuracy for many languages in the web domain. |