@inproceedings{meister-etal-2026-langmap,
title = "{L}ang{MAP}: A Language-Adaptive Approach to Tokenization",
author = "Meister, Clara and
Salhan, Suchir and
Szablewski, Andrzej and
Lesci, Pietro and
Buttery, Paula and
Pimentel, Tiago",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2170/",
pages = "49341--49362",
ISBN = "979-8-89176-459-0",
abstract = "Language-specific tokenizers improve tokenization quality and the downstream performance of models on those languages. Using one with an existing pretrained model requires either training a new model from scratch or adapting the vocabulary of the pretrained one. We propose Language-adaptive Maximum a Posteriori (LangMAP) Tokenization, which extends the UnigramLM algorithm to the multilingual setting by replacing its single distribution over the vocabulary with one distribution per language over the same vocabulary. LangMAP can be used to train a multilingual language model from scratch, or to adapt a pretrained model{'}s tokenizer to individual languages without changing its vocabulary. Language labels are required to estimate the per-language distributions; at inference, the algorithm selects the highest-probability segmentation across languages, i.e., it does not need a language label. In experiments where we adapt a pretrained tokenizer with LangMAP, we observe that LangMAP{'}s new segmentation has consistently higher morphological boundary recall than the original tokenizer. In code data, it improves alignment with abstract syntax tree (AST) leaf boundaries in all configurations we try. Despite ostensible improvements in segmentations, in language model fine-tuning experiments, we do not observe significant differences in performance for most language-task pairs."
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<abstract>Language-specific tokenizers improve tokenization quality and the downstream performance of models on those languages. Using one with an existing pretrained model requires either training a new model from scratch or adapting the vocabulary of the pretrained one. We propose Language-adaptive Maximum a Posteriori (LangMAP) Tokenization, which extends the UnigramLM algorithm to the multilingual setting by replacing its single distribution over the vocabulary with one distribution per language over the same vocabulary. LangMAP can be used to train a multilingual language model from scratch, or to adapt a pretrained model’s tokenizer to individual languages without changing its vocabulary. Language labels are required to estimate the per-language distributions; at inference, the algorithm selects the highest-probability segmentation across languages, i.e., it does not need a language label. In experiments where we adapt a pretrained tokenizer with LangMAP, we observe that LangMAP’s new segmentation has consistently higher morphological boundary recall than the original tokenizer. In code data, it improves alignment with abstract syntax tree (AST) leaf boundaries in all configurations we try. Despite ostensible improvements in segmentations, in language model fine-tuning experiments, we do not observe significant differences in performance for most language-task pairs.</abstract>
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%0 Conference Proceedings
%T LangMAP: A Language-Adaptive Approach to Tokenization
%A Meister, Clara
%A Salhan, Suchir
%A Szablewski, Andrzej
%A Lesci, Pietro
%A Buttery, Paula
%A Pimentel, Tiago
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F meister-etal-2026-langmap
%X Language-specific tokenizers improve tokenization quality and the downstream performance of models on those languages. Using one with an existing pretrained model requires either training a new model from scratch or adapting the vocabulary of the pretrained one. We propose Language-adaptive Maximum a Posteriori (LangMAP) Tokenization, which extends the UnigramLM algorithm to the multilingual setting by replacing its single distribution over the vocabulary with one distribution per language over the same vocabulary. LangMAP can be used to train a multilingual language model from scratch, or to adapt a pretrained model’s tokenizer to individual languages without changing its vocabulary. Language labels are required to estimate the per-language distributions; at inference, the algorithm selects the highest-probability segmentation across languages, i.e., it does not need a language label. In experiments where we adapt a pretrained tokenizer with LangMAP, we observe that LangMAP’s new segmentation has consistently higher morphological boundary recall than the original tokenizer. In code data, it improves alignment with abstract syntax tree (AST) leaf boundaries in all configurations we try. Despite ostensible improvements in segmentations, in language model fine-tuning experiments, we do not observe significant differences in performance for most language-task pairs.
%U https://aclanthology.org/2026.emnlp-main.2170/
%P 49341-49362
Markdown (Informal)
[LangMAP: A Language-Adaptive Approach to Tokenization](https://aclanthology.org/2026.emnlp-main.2170/) (Meister et al., EMNLP 2026)
ACL
- Clara Meister, Suchir Salhan, Andrzej Szablewski, Pietro Lesci, Paula Buttery, and Tiago Pimentel. 2026. LangMAP: A Language-Adaptive Approach to Tokenization. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 49341–49362, Budapest, Hungary. Association for Computational Linguistics.