Transfer Learning for Human & AI

Head-driven Phrase Structure Grammar (HPSG) has been found to benefit from joint training and decoding under a uniform formalism . However, decoding this unified grammar has a higher time complexity ($O(n^5)$) than decoding either form individually . We propose an improved head scorer that helpsachieve a novel performance-preserved parscher in $O$($n^3$) time complexity . We also present a more effective, morein-depth, and general work on HPSG parsing. We also explore the generalmethod of training an H PSG-based parscher from only a constituent or dependencyannotations in a multilingual scenario .

Author(s) : Zuchao Li, Junru Zhou, Hai Zhao, Kevin Parnow

Links : PDF - Abstract

Code :


Keywords : complexity - head - time - decoding - driven -

• Khan Academy Linear Algebra
• Bioinformatics Specialization on Coursera
• AI for Everyone by Andrew Ng
• Open Machine Learning Course
• Mathematics for Machine Learning Specialization
• Introduction to Python
• Spark in R using sparklyr
• Khan Academy Multivariable Calculus