59 - Weakly Supervised Semantic Parsing With Abstract Examples, with Omer Goldman

ACL 2018 paper by Omer Goldman, Veronica Latcinnik, Udi Naveh, Amir
Globerson, and Jonathan Berant Omer comes on to tell us about a
class project (done mostly by undergraduates!) that made it into
ACL. Omer and colleagues built a semantic parser that gets
state-of-the-art results on the Cornell Natural Language Visual
Reasoning dataset. They did this by using "abstract examples" -
they replaced the entities in the questions and corresponding
logical forms with their types, labeled about a hundred examples in
this abstracted formalism, and used those labels to do data
augmentation and train their parser. They also used some
interesting caching tricks, and a discriminative reranker.
https://www.semanticscholar.org/paper/Weakly-supervised-Semantic-Parsing-with-Abstract-Goldman-Latcinnik/5aec2ab5bf2979da067e2aa34762b589a0680030