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Showing 1 to 3 of 3 for “"Low-resource Nlp"”.
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Crosslingual Sharing for Low-Resource Natural Language Processing
Modern NLP systems have been highly successful at a wide variety of tasks, including language modeling and structured prediction problems such as syntactic and semantic parsing. This is due in large part to the use of supervised neural networks and more recently to unsupervised contextualized …
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Towards Data Efficiency and Controllable Representations for Deep Learning in Resource-Constrained Domains
… planetary science and healthcare, as well as in low-resource NLP settings, faces two fundamental challenges: label scarcity and data scarcity. Label scarcity stems from the high cost of expert annotation, the scarcity of domain experts, and the infeasibility of crowdsourcing, particularly in …
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Natural language processing for supporting impact assessment of funded projects
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms