all AI news
Maximum Bayes Smatch Ensemble Distillation for AMR Parsing. (arXiv:2112.07790v2 [cs.CL] UPDATED)
May 4, 2022, 1:11 a.m. | Young-Suk Lee, Ramon Fernandez Astudillo, Thanh Lam Hoang, Tahira Naseem, Radu Florian, Salim Roukos
cs.CL updates on arXiv.org arxiv.org
AMR parsing has experienced an unprecendented increase in performance in the
last three years, due to a mixture of effects including architecture
improvements and transfer learning. Self-learning techniques have also played a
role in pushing performance forward. However, for most recent high performant
parsers, the effect of self-learning and silver data augmentation seems to be
fading. In this paper we propose to overcome this diminishing returns of silver
data by combining Smatch-based ensembling techniques with ensemble
distillation. In an extensive …
More from arxiv.org / cs.CL updates on arXiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US