Papers with multilingual named entity recognition
Language Clustering for Multilingual Named Entity Recognition (2021.findings-emnlp)
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| Challenge: | Recent work in multilingual natural language processing has shown progress on tasks such as natural language inference and joint multilingual translation. |
| Approach: | They propose a technique that groups similar languages together by embeddings from a pre-trained masked language model and automatically discovering language clusters in this embeddable space. |
| Outcome: | The proposed technique outperforms baselines on 15 languages in the WikiAnn dataset showing meaningful multilingual transfer for low-resource languages (Swahili and Yoruba). |
Theoretical Linguistics Rivals Embeddings in Language Clustering for Multilingual Named Entity Recognition (2023.acl-srw)
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| Challenge: | Existing studies have used descriptive typological features and a coarse language family classification as baselines for language clustering. |
| Approach: | They propose two types of language groupings based on morpho-syntactic features in a nominal domain and one based upon a head parameter. |
| Outcome: | The proposed methods outperform state-of-the-art embedding-based models in multilingual named entity recognition (NER) . their results suggest that theoretical linguistics plays a significant role in multi-lingual learning tasks. |
Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation (P19-1)
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| Challenge: | Pretrained contextual and non-contextual subword embeddings are available in over 250 languages, allowing massively multilingual NLP. |
| Approach: | They compare pretrained contextual and non-contextual subword embeddings with a contextual representation method, namely BERT, on multilingual named entity recognition and part-of-speech tagging. |
| Outcome: | The proposed method outperforms non-contextual embeddings on multilingual named entity recognition and part-of-speech tagging. |
FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition (2026.findings-eacl)
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| Challenge: | Named entity recognition (NER) is the task of identifying tokens that belong to a predefined set of classes such as "person" or "location" |
| Approach: | They propose a dataset-creation pipeline that scales the teacher-student paradigm to 91 languages and 25 scripts. |
| Outcome: | The proposed model achieves comparable or improved performance in English, Thai, and Swahili despite being trained on 19x less data than strong baselines. |
ParaNames 1.0: Creating an Entity Name Corpus for 400+ Languages Using Wikidata (2024.lrec-main)
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| Challenge: | ParaNames is a massively multilingual parallel name resource . it provides names for 16.8 million entities in over 400 languages . |
| Approach: | They propose a massively multilingual parallel name resource with 140 million names . they use Wikidata to standardize the data and perform canonical name translation . |
| Outcome: | The proposed resource is the largest of its type to date and performs well on 10 languages. |