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Robust Lexical Features for Improved Neural Network Named-Entity Recognition
AbstractNeural network approaches to Named-Entity Recognition reduce the need for carefully hand-crafted features. While some features do remain in state-of-the-art systems, lexical features have been mostly discarded, with the exception …...
Reliability-aware Dynamic Feature Composition for Name Tagging
AbstractWord embeddings are widely used on a variety of tasks and can substantially improve the performance. However, their quality is not consistent throughout the vocabulary due to the long-tail distribution …...
Dice Loss for Data-imbalanced NLP Tasks
AbstractMany NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of easy-negative …...
Semantic-aware Chinese Zero Pronoun Resolution with Pre-trained Semantic Dependency Parser
AbstractDeep learning-based Chinese zero pronoun resolution model has achieved better performance than traditional machine learning-based model. However, the existing work related to Chinese zero pronoun resolution has not yet well …...
Subsequence Based Deep Active Learning for Named Entity Recognition
AbstractActive Learning (AL) has been successfully applied to Deep Learning in order to drastically reduce the amount of data required to achieve high performance. Previous works have shown that lightweight …...
Practical Takes on Federated Learning with Pretrained Language Models
AbstractReal-world applications of language models entail data privacy constraints when learning from diverse data domains. Federated learning with pretrained language models for language tasks has been gaining attention lately but …...