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). |
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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. |
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. |
Exploring Nested Named Entity Recognition with Large Language Models: Methods, Challenges, and Insights (2024.emnlp-main)
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| Challenge: | Named entity recognition (NER) is a challenging task in natural language processing . nested NER requires sophisticated techniques to identify entities within entities . |
| Approach: | They investigate the application of Large Language Models (LLMs) to nested NER . they find methodologies from previous work are less effective . |
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Building Named Entity Recognition Taggers via Parallel Corpora (L18-1)
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| Challenge: | Existing methods to generate semantic processors for languages lacking hand curated data are inefficiently slow and unaffordable in terms of human resources and economic costs. |
| Approach: | They propose to use statistical word alignments to project annotations from multiple sources to a target language. |
| Outcome: | The proposed method is effective to transport NER annotations across languages . it can generate a good statistical model for a new target language . |
Sources of Transfer in Multilingual Named Entity Recognition (2020.acl-main)
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| 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. |
Entity Projection via Machine Translation for Cross-Lingual NER (D19-1)
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| Challenge: | a subset of languages have large annotated corpora for named entity recognition. |
| Approach: | They propose a system that leverages machine translation systems twice to improve named entity recognition. |
| Outcome: | The proposed system outperforms existing methods on Armenian languages by 4.1 points . it achieves state-of-the-art F_1 scores for Armenian, outperforming monolingual model trained on Armenia. |
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. |
A Closer Look at Clustering Bilingual Comparable Corpora (2024.lrec-main)
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| Challenge: | Existing methods for clustering comparable corpora are not suitable for bilingual corpors. |
| Approach: | They propose new clustering models fully adapted to comparable corpora based on a deep variant of Kmeans . they illustrate their behavior on bilingual collections created from Wikipedia . |
| Outcome: | The proposed models show that they can cluster comparable corpora on bilingual collections . the proposed models are based on a state-of-the-art deep variant of Kmeans . |
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. |
Improving Multilingual Models with Language-Clustered Vocabularies (2020.emnlp-main)
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| Challenge: | State-of-the-art multilingual models depend on vocabularies that cover all languages . but the methods for generating those vocalaries are not ideal for massively multilingual applications. |
| Approach: | They propose a procedure for multilingual vocabulary generation that combines separately trained vocabularies of several automatically derived language clusters. |
| Outcome: | The proposed procedure shows improvements across languages on multilingual benchmark tasks . the proposed procedure reduces out-of-vocabulary rate by a factor of 8 . |