| Challenge: | naive training of named-entity recognition models using annotated data from multiple languages consistently underperforms monolingual models. |
| Approach: | They propose a polyglot named-entity recognition model where one model is trained using annotated data drawn from multiple languages. |
| Outcome: | The proposed model outperforms models trained on monolingual data despite more training data . the proposed model shares many parameters across languages and fine-tunes them to outperFORM monolingual models. |
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What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis (D19-1)
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| Challenge: | Named entity recognition models are challenging for languages with little training data. |
| Approach: | They propose a simple and efficient neural architecture for cross-lingual named entity recognition models. |
| Outcome: | The proposed model achieves competitive performance with the state-of-the-art on two transferable factors: sequential order and multilingual embedding. |
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers (D19-1)
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| Challenge: | Named entity recognition models rely on large amounts of labeled data, making them challenging to extend to new, lower-resource languages. |
| Approach: | They propose a method for bootstrapping named entity recognition models in under-resourced languages . they use cross-lingual transfer learning and targeted annotation of only uncertain entities . |
| Outcome: | The proposed method achieves competitive accuracy with just one-tenth of training data. |
Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages (2024.naacl-srw)
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| Challenge: | Named Entity Recognition (NER) is a useful component in NLP applications. |
| Approach: | They propose to use annotated named entity corpora to classify a given entity into a category within a textual document. |
| Outcome: | The proposed model achieves an F1 score of 0.80 on an unseen dataset for Indian languages. |
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. |
Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER (P18-2)
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| Challenge: | Existing approaches to improve NER performance add training data from one or more assisting languages to the primary language. |
| Approach: | They propose a metric based on symmetric KL divergence to filter out highly divergent training instances in the assisting language. |
| Outcome: | The proposed method improves NER performance in many languages, including those with limited training data. |
Massively Multilingual Transfer for NER (P19-1)
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| Challenge: | Existing approaches for cross-lingual transfer use a single source language, but there are exceptions. |
| Approach: | They propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively. |
| Outcome: | The proposed methods are much more effective than baseline models and rival oracle selection of the single best individual model. |
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). |
Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark (2024.naacl-long)
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Stephen Mayhew, Terra Blevins, Shuheng Liu, Marek Suppa, Hila Gonen, Joseph Marvin Imperial, Börje Karlsson, Peiqin Lin, Nikola Ljubešić, Lester James Miranda, Barbara Plank, Arij Riabi, Yuval Pinter
| Challenge: | In named entity recognition, the majority of annotation efforts are centered on English, and cross-lingual transfer performance remains brittle. |
| Approach: | They propose to develop gold-standard named entity recognition benchmarks in many languages using a cross-lingual consistent schema. |
| Outcome: | The proposed benchmarks will be released to the public in 2022 . they will provide baselines on in-language and cross-lingual learning settings. |
MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation) (2022.findings-naacl)
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| Challenge: | Named Entity Recognition (NER) is a process of identifying named entities in unstructured texts and classifying them through specific semantic categories. |
| Approach: | They propose a method for automatically producing NER annotations and introduce a manually-annotated test set. |
| Outcome: | The proposed method covers 10 languages, 15 NER categories and 2 textual genres and a manually-annotated test set. |
Towards a Unified Multi-Domain Multilingual Named Entity Recognition Model (2023.eacl-main)
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Mayank Kulkarni, Daniel Preotiuc-Pietro, Karthik Radhakrishnan, Genta Indra Winata, Shijie Wu, Lingjue Xie, Shaohua Yang
| Challenge: | Named Entity Recognition is a key task whose performance is sensitive to genre and language. |
| Approach: | They propose a setup for Named Entity Recognition which includes multi-domain and multilingual training and evaluation across 13 domains and 4 languages. |
| Outcome: | The proposed model improves on 13 domains and 4 languages across 13 domain and 4 language domains. |