  "Episodic Logic:Unscoped Logical Form" (EL-ULF) is a semantic representation
capturing predicate-argument structure as well as more challenging aspects of
language within the Episodic Logic formalism. We present the first learned
approach for parsing sentences into ULFs, using a growing set of annotated
examples. The results provide a strong baseline for future improvement. Our
method learns a sequence-to-sequence model for predicting the transition action
sequence within a modified cache transition system. We evaluate the efficacy of
type grammar-based constraints, a word-to-symbol lexicon, and transition system
state features in this task. Our system is available at
https://github.com/genelkim/ulf-transition-parser We also present the first
official annotated ULF dataset at
https://www.cs.rochester.edu/u/gkim21/ulf/resources/.
