59 - Weakly Supervised Semantic Parsing With Abstract Examples, with Omer Goldman - a podcast by Allen Institute for Artificial Intelligence

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

Further episodes of NLP Highlights

Further podcasts by Allen Institute for Artificial Intelligence

Website of Allen Institute for Artificial Intelligence