Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition (D19-1)
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| Challenge: | Existing work on name-switching focuses on word-level aspects but neglects subword-level characteristics shared across languages. |
| Approach: | They propose hierarchical meta-Embeddings that combine word-level and subword-level embeddings to create language-agnostic lexical representations. |
| Outcome: | The proposed model achieves state-of-the-art in English-Spanish code-switching scenarios. |
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From English to Code-Switching: Transfer Learning with Strong Morphological Clues (2020.acl-main)
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| Challenge: | Linguistic code-switching (CS) is an understudied area in natural language processing . lack of resources and annotated data makes it difficult to strive for progress in CS-related tasks. |
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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. |
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MIGRATE: Cross-Lingual Adaptation of Domain-Specific LLMs through Code-Switching and Embedding Transfer (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have advanced in many fields, but focus on English-centric models requires extensive data. |
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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. |
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| Challenge: | Code switching (CS) is a common phenomenon in written and spoken communication, but is handled poorly by many NLP applications. |
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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 . |
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