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. |
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Neural Cross-Lingual Named Entity Recognition with Minimal Resources (D18-1)
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| Challenge: | Named-entity recognition (NER) models are highly dependent on large amounts of labeled data. |
| Approach: | They propose a method that finds translations based on bilingual word embeddings . they also propose 'self-attention' which allows for a degree of flexibility with respect to word order . |
| Outcome: | The proposed method achieves state-of-the-art or competitive performance on common languages with lower resource requirements than previous approaches. |
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 . |
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Transfer Learning for Named-Entity Recognition with Neural Networks (L18-1)
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| Challenge: | Existing approaches to named-entity recognition (NER) require additional lead time for developing and fine-tuning the rules. |
| Approach: | They propose to transfer an ANN model trained on a large labeled dataset to another dataset with a limited number of labels to improve upon the state-of-the-art results for patient note de-identification. |
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Cross-lingual Transfer Learning for Japanese Named Entity Recognition (N19-2)
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| Challenge: | a recent study focuses on bootstrapping named entity models from English to Japanese . TL is a technique that overcomes linguistic differences between the target and source languages . |
| Approach: | They propose to use a deep neural network model to transfer weights between languages . they also propose a novel approach that romanizes a portion of the Japanese input . |
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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. |
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. |
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. |
Neural Adaptation Layers for Cross-domain Named Entity Recognition (D18-1)
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| Challenge: | Named entity recognition is a type of information extraction task whereby features can be designed based on domain-specific knowledge. |
| Approach: | They propose to use existing neural architectures to adapt to new domains without retraining . they propose to add adaptation layers to existing neural models to minimize re-training based on source data. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art methods on social media domains. |
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition (2022.emnlp-main)
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David Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel, Constantine Lignos, Jesujoba Alabi, Shamsuddeen Muhammad, Peter Nabende, Cheikh M. Bamba Dione, Andiswa Bukula, Rooweither Mabuya, Bonaventure F. P. Dossou, Blessing Sibanda, Happy Buzaaba, Jonathan Mukiibi, Godson Kalipe, Derguene Mbaye, Amelia Taylor, Fatoumata Kabore, Chris Chinenye Emezue, Anuoluwapo Aremu, Perez Ogayo, Catherine Gitau, Edwin Munkoh-Buabeng, Victoire Memdjokam Koagne, Allahsera Auguste Tapo, Tebogo Macucwa, Vukosi Marivate, Mboning Tchiaze Elvis, Tajuddeen Gwadabe, Tosin Adewumi, Orevaoghene Ahia, Joyce Nakatumba-Nabende, Neo Lerato Mokono, Ignatius Ezeani, Chiamaka Chukwuneke, Mofetoluwa Oluwaseun Adeyemi, Gilles Quentin Hacheme, Idris Abdulmumin, Odunayo Ogundepo, Oreen Yousuf, Tatiana Moteu, Dietrich Klakow
| Challenge: | Existing studies on named entity recognition methods for African languages focus on English as the source language, but there is evidence that it is not the best for low-resource languages. |
| Approach: | They propose to use human-annotated datasets to analyze named entity recognition tasks in 20 African languages to test whether they are effective. |
| Outcome: | The proposed method improves zero-shot F1 scores by 14% over 20 languages compared to using English . |
Data Augmentation for Cross-Domain Named Entity Recognition (2021.emnlp-main)
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| Challenge: | Existing methods for named entity recognition focus on augmenting in-domain data in low-resource scenarios where annotated data is limited. |
| Approach: | They propose a neural architecture to transform data from high-resource to low-resourced domains by learning the patterns in the text that differentiate them. |
| Outcome: | The proposed approach improves on high-resource domain representations over high- and low-resourced domains. |