Trainable, Multiword-aware Linguistic Tokenization Using Modern Neural Networks (2026.eacl-srw)
Copied to clipboard
| Challenge: | Tokenization is a fundamental task in natural language processing that forms the first step of many pipelines. |
| Approach: | They propose to use a standard tokenizer trained without MWE-awareness as a baseline and a character-level SRN+CRF model to train token-level models. |
| Outcome: | The proposed tokenizers are based on a character-level and token-level sequence labeling problem and are consistent with the proposed pipelines. |
Similar Papers
Multilingual Word Segmentation: Training Many Language-Specific Tokenizers Smoothly Thanks to the Universal Dependencies Corpus (L18-1)
Copied to clipboard
| Challenge: | Towards language scalability, major progress has been achieved in multilingual language technology in recent years. |
| Approach: | They propose a tokenizer that can be trained from any Universal Dependencies corpus dataset . they argue that tokenization should be seen as a supervised task and scalability requires a software engineering process across languages. |
| Outcome: | The proposed tokenizer can be trained from any dataset in the corpus UD2 . the proposed software tool relies on elephant to perform the training . |
Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Models (2026.eacl-long)
Copied to clipboard
| Challenge: | Subword tokenization approaches misalign with linguistic structure and waste capacity across languages and domains. |
| Approach: | They argue for a context-aware framework that integrates tokenizer and model co-design . they argue that tokenization should be treated as a core design problem, not an afterthought . |
| Outcome: | The proposed framework integrates tokenizer and model co-design, guided by linguistic, domain, and deployment considerations. |
Where are we Still Split on Tokenization? (2024.findings-eacl)
Copied to clipboard
| Challenge: | Identifying tokens is a crucial first step for many tasks in Natural Language Processing (NLP) gold tokenization is often assumed, but some work on token-level tasks is more challenging. |
| Approach: | They propose an efficient method for tokenization with subword-based language models and evaluate it on 122 languages in 20 scripts. |
| Outcome: | The proposed method performs on par with the state-of-the-art on 122 languages in 20 scripts. |
What is the best recipe for character-level encoder-only modelling? (2023.acl-long)
Copied to clipboard
| Challenge: | aims to benchmark recent progress in language understanding models that output contextualised representations at the character level. |
| Approach: | They aim to find the best way to build and train character-level BERT-like models by comparing architectural innovations with pretraining objectives. |
| Outcome: | The proposed model outperforms a token-based model on a set of evaluation tasks with a fixed training procedure. |
A Multi-dimensional Evaluation of Tokenizer-free Multilingual Pretrained Models (2023.findings-eacl)
Copied to clipboard
| Challenge: | Recent work on tokenizer-free models shows promising results in cross-lingual transfer . previous work focused on reporting accuracy on a limited set of tasks and data settings . |
| Approach: | They compare tokenizer-free and subword-based models using various dimensions . they find subword models are still the most practical choice in many settings . |
| Outcome: | The proposed model improves cross-lingual transfer and reduces engineering overhead. |
Multilingual Tokenization through the Lens of Indian Languages: Challenges and Insights (2026.findings-acl)
Copied to clipboard
Maharaj Brahma, N J Karthika, Rajat Verma, Nagasai Saketh Naidu, Rohit Saluja, Maunendra Sankar Desarkar, Ganesh Ramakrishnan
| Challenge: | Existing tokenizers are often skewed towards high-resource languages limiting their effectiveness for linguistically diverse and morphologically rich languages. |
| Approach: | They evaluate multilingual tokenization across 17 Indic languages spanning 11 scripts and two language families. |
| Outcome: | The proposed method improves tokenization quality and vocabulary size in 17 languages . poor tokenization can lead to increase in sequence lengths, fragment meaningful units, weaken model's ability to capture linguistic structure and semantics. |
Beyond Text Compression: Evaluating Tokenizers Across Scales (2025.acl-long)
Copied to clipboard
| Challenge: | Language models rely on tokenizers to convert text into machine-interpretable tokens, which shape the statistical patterns that language models learn to estimate. |
| Approach: | They propose to use Zipf's law to measure tokenizer performance by combining several metrics to capture multiple aspects of tokenizer behavior. |
| Outcome: | The proposed metrics correlate more strongly with downstream performance than text compression when modeling unseen languages. |
Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation (2022.tacl-1)
Copied to clipboard
| Challenge: | End-to-end neural models have replaced the traditional pipeline and require an explicit tokenization step. |
| Approach: | They propose a neural encoder that operates directly on character sequences without explicit tokenization or vocabulary and a pre-training strategy that optionally uses subwords as a soft inductive bias. |
| Outcome: | The proposed model outperforms a comparable mBert model on a multilingual benchmark by 5.7 F1 on the TyDi QA benchmark. |
Phonemes to the Rescue: Multilingual Tokenization Based on International Phonetic Alphabet (2026.acl-long)
Copied to clipboard
| Challenge: | Widely-used subword tokenization approaches favor high-resource languages and tokenizer-free methods yield longer sequences for scripts with a higher bytes-per-character ratio. |
| Approach: | They propose to use the International Phonetic Alphabet (IPA) as a language-agnostic input representation for multilingual tokenizers. |
| Outcome: | The proposed model improves tokenization quality and generalizes more effectively to unseen languages and scripts. |
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models (2021.acl-long)
Copied to clipboard
| Challenge: | Using pretraining data, we find that a designated monolingual tokenizer plays an equally important role in the downstream performance of the model. |
| Approach: | They propose to compare pretrained multilingual models with their monolingual counterparts on a set of five diverse monolingual downstream tasks. |
| Outcome: | The proposed models offer previously unmatched performance in all NLP tasks. |