11 citations · 15 across the 4 of their papers we have counts for
4 papers
Teaching Machine Comprehension with Compositional Explanations
Qinyuan Ye, Xiao Huang, Elizabeth Boschee +1
Advances in machine reading comprehension (MRC) rely heavily on the collection of large scale human-annotated examples in the form of (question, paragraph, answer) triples. In cont…
TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
Bill Yuchen Lin, Dong-Ho Lee, Ming Shen +4
Training neural models for named entity recognition (NER) in a new domain often requires additional human annotations (e.g., tens of thousands of labeled instances) that are usuall…
Learning A Unified Named Entity Tagger From Multiple Partially Annotated Corpora For Efficient Adaptation
Xiao Huang, Li Dong, Elizabeth Boschee +1
Named entity recognition (NER) identifies typed entity mentions in raw text. While the task is well-established, there is no universally used tagset: often, datasets are annotated…
Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling
Ouyu Lan, Xiao Huang, Bill Yuchen Lin +3
Sequence labeling is a fundamental framework for various natural language processing problems. Its performance is largely influenced by the annotation quality and quantity in super…