Papers with language clustering
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. |
Meta-XNLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation (2022.findings-acl)
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| Challenge: | Existing approaches to learn shareable structures from low-resource languages are limited in the zero-shot setting. |
| Approach: | They propose a meta-learning framework to learn shareable structures from typologically diverse languages based on meta- learning and language clustering. |
| Outcome: | The proposed framework is able to learn shareable structures from typologically diverse languages with limited annotated data. |
Multilingual Neural Machine Translation with Language Clustering (D19-1)
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| Challenge: | Existing work on multilingual neural machine translation has been neglected due to its burdensome training process. |
| Approach: | They develop a framework that clusters languages into different groups and trains one multilingual model for each cluster. |
| Outcome: | The proposed model reduces the cost of training and improves translation accuracy. |
Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations (2020.emnlp-main)
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| Challenge: | Recent studies consider linguistic typology as a potential source of knowledge to support multilingual natural language processing (NLP) tasks. |
| Approach: | They propose to fuse both views using canonical correlation analysis and use it to infer typological features and language phylogenies to construct a multi-view language vector space for multilingual machine translation. |
| Outcome: | The proposed model achieves competitive translation accuracy in multilingual machine translation tasks without expensive retraining of massive multilingual or ranking models. |