Augmented Natural Language for Generative Sequence Labeling
Ben Athiwaratkun, Cícero Nogueira dos Santos, Jason Krone, Bing Xiang · 2020
We propose a generative framework for joint sequence labeling and sentence-level classification.Our model performs multiple sequence labeling tasks at once using a single, shared natural language output space.Unlike prior discriminative methods, our model naturally incorporates label semantics and shares knowledge across tasks.Our framework is general purpose, performing well on fewshot, low-resource, and high-resource tasks.We demonstrate these advantages on popular named entity recognition, slot labeling, and intent classification benchmarks.We set a new state-of-the-art for few-shot slot labeling, improving substantially upon the previous 5-shot (75.0%!90.9%) and 1-shot (70.4% !81.0%) state-of-the-art results.Furthermore, our model generates large improvements (46.27% !63.83%) in low-resource slot labeling over a BERT baseline by incorporating label semantics.We also maintain competitive results on high-resource tasks, performing within two points of the state-of-theart on all tasks and setting a new state-of-theart on the SNIPS dataset.