Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework

no code implementations • Findings (EMNLP) 2021 • Yaqing Wang, Haoda Chu, Chao Zhang, Jing Gao

In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings.

Few-Shot Learning named-entity-recognition +3

Adaptive Self-training for Neural Sequence Labeling with Few Labels

no code implementations • 1 Jan 2021 • Yaqing Wang, Subhabrata Mukherjee, Haoda Chu, Yuancheng Tu, Ming Wu, Jing Gao, Ahmed Hassan Awadallah

Neural sequence labeling is an important technique employed for many Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), slot tagging for dialog systems and semantic parsing.

Meta-Learning named-entity-recognition +3

Adaptive Self-training for Few-shot Neural Sequence Labeling

no code implementations • 7 Oct 2020 • Yaqing Wang, Subhabrata Mukherjee, Haoda Chu, Yuancheng Tu, Ming Wu, Jing Gao, Ahmed Hassan Awadallah

While self-training serves as an effective mechanism to learn from large amounts of unlabeled data -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.

Meta-Learning named-entity-recognition +3